CN116811628A - Comprehensive energy system containing electric automobile charging and ordered charging method - Google Patents
Comprehensive energy system containing electric automobile charging and ordered charging method Download PDFInfo
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/30—Constructional details of charging stations
- B60L53/31—Charging columns specially adapted for electric vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/50—Charging stations characterised by energy-storage or power-generation means
- B60L53/51—Photovoltaic means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/66—Data transfer between charging stations and vehicles
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Abstract
The invention relates to a comprehensive energy system containing electric automobile charging and an ordered charging method, wherein the comprehensive energy system comprises an energy storage system, an energy storage converter, photovoltaic power generation equipment, a photovoltaic inverter, a charging pile, a load system, an energy management controller and an energy management center; the energy management controller establishes communication with the energy storage system, the energy storage converter, the photovoltaic power generation equipment, the photovoltaic inverter, the charging pile and the load system, and performs information interaction with the energy management center; the energy management center uses the running state information of each device uploaded by the energy management controller as a data basis, uses the charging cost, the charging convenience and the load fluctuation degree of the power grid of a user as optimization targets, optimizes the initial charging time of the electric automobile by improving a particle swarm algorithm, and accurately transmits the initial charging time to a charging pile to be controlled by the energy management controller. The comprehensive energy system and the ordered charging method are beneficial to optimizing the charging time of the electric automobile and realizing ordered charging of the electric automobile.
Description
Technical Field
The invention relates to the technical field of energy management, in particular to a comprehensive energy system containing electric automobile charging and an ordered charging method.
Background
Renewable energy sources are widely applied due to the advantages of green, sustainable development, improvement of excessive exploitation of fossil energy sources, environmental pollution caused by unreasonable use and the like, but the problems of high cost and low energy utilization rate of an electric power system caused by uncertainty and uncontrollable power generation and the like are solved. The comprehensive energy system can be coupled with renewable energy sources, distributed power sources and the like, so that a large amount of the distributed power sources and the renewable energy sources can be promoted to be connected into a power grid, meanwhile, the complementary mutual-aid among multiple types of energy sources can improve the reliability of energy source supply, and the energy utilization rate is effectively improved while the system load requirement is met.
However, with the promotion of national policy and the development of electric automobile technology, more electric automobiles are connected into a power system. When large-scale electric vehicles are charged according to personal habits in a disordered manner, on one hand, the charging load peak time is consistent with the base load peak time, the situation that the power grid stably operates is not facilitated due to peak-to-peak adding and the like occurs, and on the other hand, the uncertainty of the charging load in time and space is increased, the uncertainty of the total load demand is increased, and the optimization effect of energy management is limited.
Disclosure of Invention
The invention aims to provide a comprehensive energy system containing electric vehicle charging and an ordered charging method.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: the comprehensive energy system comprises an energy storage system, an energy storage converter, photovoltaic power generation equipment, a photovoltaic inverter, a charging pile, a load system, an energy management controller and an energy management center, wherein the energy storage system is used for charging an electric automobile; the energy management controller establishes communication connection with the energy storage system, the energy storage converter, the photovoltaic power generation equipment, the photovoltaic inverter, the charging pile and the load system through RS485 communication, and performs information interaction with the energy management center through the Ethernet; the energy management center optimizes the initial charging time of the electric automobile by improving a particle swarm algorithm based on the running state information of each device uploaded by the energy management controller stored in the database as a data base and on the charging cost, the charging convenience and the load fluctuation degree of the power grid of a user, determines the address issued by a signal according to a communication protocol point table according to an optimization result, and accurately issues the address to a charging pile to be controlled by the energy management controller so as to realize ordered charging of the electric automobile.
Further, the energy storage system comprises an energy storage battery and a BMS battery management system, wherein the energy storage battery stores and releases electric energy through charge and discharge, and output direct current realizes conversion of alternating current and direct current through an energy storage converter; the BMS battery management system collects information of total voltage and total current of the battery pack, highest voltage, lowest voltage and temperature of the single battery in real time, and estimates the state of charge (SOC) and the state of health (SOH) of the battery pack according to the collected information; the controller of the energy storage converter receives a background control instruction through communication and controls the energy storage converter to charge or discharge an energy storage battery in a P/Q mode; the controller of the energy storage converter is communicated with the BMS battery management system through RS485 communication to acquire battery pack state information, realize protective charge and discharge of the battery and ensure battery operation safety.
Further, the charging pile is a unidirectional alternating current charging pile and is used for supporting the electric automobile to charge with constant power; the DSP core control board is arranged in the charging pile, so that the state information of the electric automobile connected to the charging pile can be uploaded to the energy management controller, and signals sent by the energy management controller are received to set the charging start time and the charging end time of the charging pile.
Further, the energy management controller comprises a plurality of types of input/output interfaces, is connected with an energy storage system, an energy storage converter, photovoltaic power generation equipment, a photovoltaic inverter, a charging pile and a load system through RS485 communication, and is communicated by adopting a standard MODBUS RTU communication protocol; the DSP core control board of each device exchanges information with the energy management controller at regular intervals to realize the monitoring and control of the energy management controller on the running state of the comprehensive energy system; the energy management controller establishes communication with the energy management center through the Ethernet, uploads signals to the energy management center and accurately transmits the signals transmitted by the energy management center.
Further, the energy management center is connected with the energy management controller through an Ethernet, and the data of the energy management controller are read according to IEC60870-104 standard communication and stored in an SQL server database; the energy management center is provided with a software program for realizing an ordered charging method, the ordered charging method is developed based on MATLAB, an ODBC data source is configured in a Windows operating system to be connected with an SQL Server database, and running state information of each device is obtained; and determining an address corresponding to the control signal according to the ordered charging optimization result by the MODBUS protocol point table, accurately issuing the address to a charging pile DSP core control board to be controlled by the energy management controller, and controlling the initial charging time and the end charging time of the charging pile DSP core control board to be controlled.
The invention also provides an ordered charging method based on the integrated energy system containing the electric automobile charging, which comprises the following steps:
step S1: a Monte Carlo method is adopted to establish a charge load typical curve of the electric automobile in a disordered charge state;
step S2: establishing an ordered charging model considering the charging cost, the charging convenience and the power grid load fluctuation degree of a user;
step S3: dynamically weighting the ordered charging objective function according to the base load variance and weather type predicted in the day-ahead;
step S4: and solving an ordered charging objective function meeting constraint conditions by adopting an improved particle swarm algorithm, and optimizing the initial charging time of the electric automobile.
Further, the implementation method of the step S1 is as follows:
firstly, analyzing the driving characteristics of the electric automobile, wherein the travel end time, the first travel time and the daily driving mileage of the electric automobile meet the following probability density functions:
wherein mu is D 、σ d 、μ e 、σ e Mu and sigma are respectively the expected and standard deviation of the travel mileage, the first travel time and the travel end time; according to the daily driving mileage data and the automobile battery parameters, calculating the initial charge state when the automobile is charged, wherein the calculation formula is as follows:
wherein i represents the i-th electric vehicle, SOC end,i State of charge, SOC indicating end of charge start,i Representing the state of charge of the initial charge, T c_all,i Indicating the total charge time, w ev Represents the battery power consumed when the electric automobile runs for hundred km, E ev Represents the battery capacity of the electric automobile, lambda ev_eff Charge efficiency for battery, p charge Representing the charging power of the electric automobile;
starting to charge to a disordered charge mode at the end time of the stroke; combining the formal characteristics of the electric automobile and the calculation formula of the charging duration, and adopting a Monte Carlo method to simulate the initial charging time, the daily driving mileage and the first travel time of the electric automobile to obtain a typical curve of the charging load of the electric automobile in a disordered charging state; and adopting a Monte Carlo method to perform multiple simulation to obtain an average value, and obtaining a charging load typical curve of the electric automobile in a disordered charging state.
Further, in step S2, the objective function of the charging cost of the user side is:
wherein m represents the charging quantity of the electric automobile, T represents the scheduling period, S (T) represents the charging electricity price of the electric automobile at the moment T, and P i t The charging power of the ith electric automobile at the t moment is shown;
the power grid side takes the load fluctuation degree as an optimization target, and an objective function of the power grid load fluctuation degree is as follows:
Wherein P is load_mean Average value P representing sum of system base load and electric vehicle charging load 0 (t) represents the base load at time t, obtained by prediction before the day,representing the charging load of m electric vehicles at time t, V 2 Representing the power grid load variance;
the user charging convenience reflects the personal habit change degree, and the change quantity of the charging electric quantity in each period is measured:
wherein f 0 (t) represents the charging load of the electric vehicle at the time t before the ordered charge control, and f (t) represents the charging load at the time t after the ordered charge control;
taking the difference of dimension and unit of charging cost and power grid load fluctuation degree into consideration, carrying out normalization processing on an objective function and giving a weight coefficient, wherein the formula is as follows:
y ev ＝λ 1 Q+λ 2 Z+λ 3 B
wherein q (t) 0 ) max Representing the maximum charging cost of the electric automobile before orderly control, V 2 max Representing maximum grid load variance, lambda, before ordered charging 1 、λ 2 And lambda (lambda) 3 Weights respectively representing 3 optimization targets, each reflecting the proportion occupied by the charge cost of the user, the fluctuation degree of the power grid load and the charge convenience of the user in the optimization model, lambda 1 、λ 2 And lambda (lambda) 3 The sum of (2) is 1;
constraint conditions of the ordered charging model are as follows:
the initial charging time constraint of the electric automobile; when the initial charging of the electric automobile is optimized, the charging time of the automobile owner is required to be after the travel time of the electric automobile is finished and before the first trip time of the next day, and the charge state of the automobile owner needs to be ensured to meet the requirement of the automobile owner, so the initial charging time t of the electric automobile is required start,i The following constraints are satisfied:
wherein i represents the i-th electric vehicle, SOC ref,i Indicating that the vehicle owner expects to reach the charge state, t need,i Indicating the charging time, t end,i The time representing the first trip is the latest charging time, t start_inial,i Indicating the stroke end time;
continuous constraint of the battery charge state of the electric automobile; in the charging process of the electric automobile battery, the SOC update meets the following constraint:
in SOC t+1,i Representing the state of charge at time t+1, SOC t,i Representing the state of charge at time T, T C,t,i The charging time at the time t is represented;
upper limit constraint of power grid load; the sum of the base load and the charging load of the electric automobile in the corresponding region cannot exceed the maximum capacity of the transformer.
Further, the implementation method of step S3 is as follows:
according to the average value comparison result of the daily predicted base load variance and the base load variance in the historical data, combining the predicted weather types, constructing the following four scenes and respectively setting the weight distribution values of the objective function:
scene 1: the base load data variance value predicted before the day is larger than the base load variance average value in the historical data, and the meteorological data is displayed as a sunny day;
scene 2: the base load data variance value predicted before the day is larger than the base load variance average value in the historical data, and the meteorological data are displayed as overcast and rainy days;
Scene 3: the base load data variance value predicted before the day is smaller than the base load variance average value in the historical data, and the weather data report result is a sunny day;
scene 4: the base load data variance value predicted before the day is smaller than the base load variance average value in the historical data, and the meteorological data are displayed as overcast and rainy days;
according to the scene, when the variance of the daily predicted base load data is larger, the improvement of the load fluctuation degree of the power grid is more focused, and the lambda is improved 2 A numerical value; when weather data shows that weather is overcast and rainy days, users pay more attention to convenience, more users adopt a random charging mode of stopping and charging, and lambda is improved 3 A numerical value; user cost minimization is an important factor in the optimization process, is also a key for exciting users to participate in ordered charging, and has higher weight under different charging scenes.
Further, the implementation method of step S4 is as follows:
initializing an improved particle swarm algorithm and parameters of the electric automobile; initializing an initial charging time sequence of the electric automobile group to be a particle group initial position according to model parameters, constraint conditions and a disordered charging load typical curve, and taking an ordered charging objective function as an adaptation function of an improved particle group algorithm; the position of the next generation particle, i.e. the charging time distribution, is updated in combination with the following particle formula:
v i (t+1)＝ω*v i (t)+r 1 *c 1 *(pbest(t)-x i (t))+r 2 *c 2 *(gbest(t)-x i (t))
x i (t+1)＝x i (t)+v i (t+1)
In the particle updating process, an updating mechanism for adaptively updating inertia weight and learning factors is adopted to adjust the two parameters, so that the conventional particle swarm algorithm is prevented from being trapped into local optimization; outputting the particles which finally meet the conditions, namely the initial charging time sequence, as the ordered charging time of the electric automobile;
the updating mechanism for adaptively updating the inertia weight and the learning factor is as follows:
wherein a is the population of particles, f p (x i (t)) represents the size of the order charge objective function, λ (t), which is the fitness of the ith particle, the t iterationRepresenting the smoothness of the change of the weight of the t-th iteration;
wherein f p Representing the fitness of each iteration particle, namely the size of an ordered charge objective function, f ave Mean value of ordered charge objective function, f, representing fitness of particle swarm min Representing the fitness of the particle swarm, namely the minimum value of the ordered charge objective function, the strategy increases the pertinence of learning factor updating.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the charging cost, the charging convenience and the load fluctuation degree of the power grid of the user are taken as optimization targets, so that the peak-valley difference of the power grid caused by disordered charging is effectively improved, the charging cost of the user is reduced, and the charging convenience of the user is ensured in overcast and rainy days.
2. According to the application, different scenes are constructed according to the magnitude of the daily predicted base load variance and the weather type, and the objective function of ordered charging is dynamically weighted, so that the diversity requirements of the user side and the power grid side under different scenes are met.
3. According to the application, the initial charging time of the electric automobile is optimized by adopting the particle swarm algorithm capable of adaptively updating the inertia weight and the learning factor, the defect that the conventional particle swarm algorithm is easy to fall into local optimization is overcome, and the optimization effect of ordered charging of the electric automobile is improved.
Drawings
FIG. 1 is a schematic diagram of an integrated energy system according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of an orderly charging method according to an embodiment of the application;
FIG. 3 is a flowchart of an implementation of optimizing charging time of an electric vehicle using an improved particle swarm algorithm in an embodiment of the application;
fig. 4 is a diagram of an ordered charge optimization result in an embodiment of the present application.
Detailed Description
The application will be further described with reference to the accompanying drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The embodiment provides a comprehensive energy system comprising electric automobile charging, and the structure of the comprehensive energy system is shown in fig. 1. The comprehensive energy system comprises an energy storage system, an energy storage converter, photovoltaic power generation equipment, a photovoltaic inverter, a charging pile, a load system, an energy management controller and an energy management center. The energy management controller establishes communication with the energy storage system, the energy storage converter, the photovoltaic power generation equipment, the photovoltaic inverter, the charging pile and the load system through RS485 communication, and performs information interaction with the energy management center through the Ethernet. The energy management center optimizes the initial charging time of the electric automobile by improving a particle swarm algorithm based on running state information of each device such as a charging pile and the like which are uploaded by the energy management controller and stored in the database, and on the charging cost, the charging convenience and the load fluctuation degree of the power grid of a user, and then determines the address issued by a signal according to a communication protocol point table by the optimized result, and accurately issues the address to the charging pile to be controlled by the energy management controller, so that the ordered charging of the electric automobile is realized.
In this embodiment, the energy storage system includes an energy storage battery and a BMS battery management system, where the energy storage battery stores and releases electric energy through charge and discharge, and the output direct current realizes ac-dc conversion through an energy storage converter; the BMS battery management system collects information such as total voltage and total current of the battery pack, highest voltage, lowest voltage and temperature of the single battery and the like in real time, and estimates the state of charge (SOC) and the state of health (SOH) of the battery pack according to the collected information; the controller of the energy storage converter receives a background control instruction through communication and controls the energy storage converter to charge or discharge an energy storage battery in a P/Q mode; the controller of the energy storage converter is communicated with the BMS battery management system through RS485 communication to acquire battery pack state information, realize protective charge and discharge of the battery and ensure battery operation safety.
In this embodiment, the charging pile is a unidirectional ac charging pile, which supports charging the electric vehicle with constant power; the DSP core control board is arranged in the charging pile, so that the state information of the electric automobile connected to the charging pile can be uploaded to the energy management controller, and signals sent by the energy management controller are received to set the charging start time and the charging end time of the charging pile.
In this embodiment, the energy management controller includes multiple types of input/output interfaces, and is connected to the energy storage system, the energy storage converter, the photovoltaic power generation device, the photovoltaic inverter, the charging pile, and the DSP core control board of the load system through RS485 communication, and uses a standard MODBUS RTU communication protocol to perform communication; the DSP core control board of each device exchanges information with the energy management controller at regular intervals to realize the monitoring and control of the energy management controller on the running state of the comprehensive energy system; the energy management controller establishes communication with the energy management center through the Ethernet, uploads signals to the energy management center and accurately transmits the signals transmitted by the energy management center.
In the embodiment, the energy management center is connected with the energy management controller through an Ethernet, and the data of the energy management controller is read according to IEC60870-104 standard communication and stored in an SQL server database; the energy management center is provided with a software program for realizing an ordered charging method, the ordered charging method is developed based on MATLAB, an ODBC data source is configured in a Windows operating system to be connected with an SQL Server database, and running state information of various devices such as a charging pile is obtained; and determining an address corresponding to the control signal according to the ordered charging optimization result by the MODBUS protocol point table, accurately issuing the address to a charging pile DSP core control board to be controlled by the energy management controller, and controlling the initial charging time and the end charging time of the charging pile DSP core control board to be controlled.
The embodiment also provides an orderly charging method based on the integrated energy system containing the electric automobile charging, and the flow of the orderly charging method is shown in fig. 2. Firstly, the energy management controller collects information such as running states, parameters and the like of all equipment of the system, and uploads the information to an SQL server database of an energy management center for storage, so that a data base is provided. And orderly charging optimization is carried out on the basis of the database in the energy management center, and finally the energy management controller transmits the charging time of the electric vehicle optimized by the energy management center to the charging pile, acquires and uploads the state parameters of the electric vehicle in real time, and the energy management center determines that the charging time is ended and transmits the charging time.
The orderly charging method specifically comprises the following steps:
step S1: and a Monte Carlo method is adopted to establish a charge load typical curve of the electric automobile in a disordered charge state.
Step S2: and establishing an ordered charging model considering the charging cost, the charging convenience and the fluctuation degree of the power grid load of the user.
Step S3: the ordered charge objective function is dynamically weighted according to the magnitude of the base load variance and weather type predicted in the day-ahead.
Step S4: and solving an ordered charging objective function meeting constraint conditions by adopting an improved particle swarm algorithm, and optimizing the initial charging time of the electric automobile.
In step S1, the driving characteristics of the electric vehicle are analyzed first, and the travel end time of the electric vehicle, i.e., the earliest charging start time, the first travel time, i.e., the latest charging end time, and the daily driving mileage satisfy the following probability density functions:
wherein mu is D 、σ d 、μ e 、σ e μ, σ are the expected and standard deviations of the travel mileage, the first trip time and the trip end time, respectively.
According to the daily driving mileage data and the automobile battery parameters, calculating the initial charge state when the automobile is charged, wherein the calculation formula is as follows:
wherein i represents the i-th electric vehicle, SOC end,i State of charge, SOC indicating end of charge start,i Representing the state of charge of the initial charge, T c_all,i Indicating the total charge time, w ev Represents the battery power consumed when the electric automobile runs for hundred km, E ev Represents the battery capacity of the electric automobile, lambda ev_eff Charge efficiency for battery, p charge The charging power of the electric vehicle is shown.
Starting to charge to a disordered charge mode at the end time of the stroke; combining the formal characteristics of the electric automobile and the calculation formula of the charging duration, and adopting a Monte Carlo method to simulate the initial charging time, the daily driving mileage and the first travel time of the electric automobile to obtain a typical curve of the charging load of the electric automobile in a disordered charging state; and adopting a Monte Carlo method to perform multiple simulation to obtain an average value, and obtaining a charging load typical curve of the electric automobile in a disordered charging state. Here, assume that the vehicle owner charges 1 day, the charging power adopts constant charging power, the power battery capacity is averaged and the same, the electric quantity consumption of hundred km is fixed, and a continuous and uninterrupted charging mode is adopted. The day is divided into 96 time periods, each 15min as one time period.
In step S2, an ordered charging model of the electric vehicle is established. The area where the comprehensive energy system is located adopts a time-of-use electricity price mechanism, and the objective function of the charging cost of the user side is as follows:
wherein m represents the charging quantity of the electric automobile, T represents the scheduling period, S (T) represents the charging electricity price of the electric automobile at the moment T, and P i t The charging power at time t of the ith electric automobile is shown.
The power grid side takes the load fluctuation degree as an optimization target, introduces variance to describe the power grid load fluctuation degree, and an objective function of the power grid load fluctuation degree is as follows:
wherein P is load_mean Average value P representing sum of system base load and electric vehicle charging load 0 (t) represents the base load at time t, obtained by prediction before the day,representing the charging load of m electric vehicles at time t, V 2 Representing the grid load variance.
The user charging convenience reflects the personal habit change degree, and the change quantity of the charging electric quantity in each period is measured:
wherein f 0 (t) represents the charging load of the electric vehicle at the time t before the ordered charge control, and f (t) represents the charging load at the time t after the ordered charge control.
Taking the difference of dimension and unit of charging cost and power grid load fluctuation degree into consideration, carrying out normalization processing on an objective function and giving a weight coefficient, wherein the formula is as follows:
y ev ＝λ 1 Q+λ 2 Z+λ 3 B
Wherein q (t) 0 ) max Representing the maximum charging cost of the electric automobile before orderly control, V 2 max Representing maximum grid load variance, lambda, before ordered charging 1 、λ 2 And lambda (lambda) 3 Weights respectively representing 3 optimization targets, each reflecting the proportion occupied by the charge cost of the user, the fluctuation degree of the power grid load and the charge convenience of the user in the optimization model, lambda 1 、λ 2 And lambda (lambda) 3 The sum of (2) is 1.
Constraint conditions are introduced into the ordered charging objective function to perfect an ordered charging model, and the initial charging time constraint of the electric automobile, the continuity constraint of the charging state of the battery of the electric automobile and the maximum capacity constraint of the transformer are required to be met. Constraint conditions of the ordered charging model are as follows:
and (5) restraining the initial charging time of the electric automobile. When the electric vehicle is optimized to start charging, the charging time of the vehicle owner is required to be within the starting and ending charging time range of the typical curve obtained in the step S1 after the travel time of the electric vehicle is ended and before the first travel time of the next day. In addition, the ordered charging needs to be within the range of the start charging time and the end charging time, and needs to be ensuredThe state of charge meets the requirements of the vehicle owners, and generally reaches 95%. So the initial charging time t of the electric automobile start,i The following constraints are satisfied:
wherein i represents the i-th electric vehicle, SOC ref,i Indicating that the vehicle owner expects to reach the charge state, t need,i Indicating the charging time, t end,i The time representing the first trip is the latest charging time, which is obtained according to the Monte Carlo method in the step S1, t start_inial,i The stroke end time is indicated and obtained according to the monte carlo method in step S1.
Electric automobile battery state of charge continuity constraint. In the charging process of the electric automobile battery, the SOC update meets the following constraint:
in SOC t+1,i Representing the state of charge at time t+1, SOC t,i Representing the state of charge at time T, T C,t,i The charging time period at time t is indicated.
Grid load upper limit constraints. The sum of the base load and the charging load of the electric automobile in the corresponding region cannot exceed the maximum capacity of the transformer.
In step S3, according to the comparison result of the base load variance predicted in the day-ahead and the average value of the base load variance in the history data, in combination with the predicted weather type, the following four scenes are constructed and the weight distribution values of the objective function are set respectively:
scene 1: the base load data variance value predicted before the day is larger than the base load variance average value in the historical data, and the meteorological data is displayed as a sunny day;
scene 2: the base load data variance value predicted before the day is larger than the base load variance average value in the historical data, and the meteorological data are displayed as overcast and rainy days;
Scene 3: the base load data variance value predicted before the day is smaller than the base load variance average value in the historical data, and the weather data report result is a sunny day;
scene 4: the base load data variance value predicted before the day is smaller than the base load variance average value in the historical data, and the meteorological data are displayed as overcast and rainy days;
according to the scene, when the variance of the daily predicted base load data is larger, the improvement of the load fluctuation degree of the power grid is more focused, and the lambda is improved 2 A numerical value; when weather data shows that weather is overcast and rainy days, users pay more attention to convenience, more users adopt a random charging mode of stopping and charging, and lambda is improved 3 A numerical value; user cost minimization is an important factor in the optimization process, is also a key for exciting users to participate in ordered charging, and has higher weight under different charging scenes.
In the four charging scenes, weight coefficients are set according to benefits and simulation debugging which are important to consider by a user and a power grid side, and in the setting process, the user charging cost weight is always the highest, which is the premise of stimulating the user to participate in an ordered charging strategy.
The flow of solving the ordered charge model using the modified particle swarm algorithm is shown in FIG. 3. The specific implementation method of the step S4 is as follows:
And optimizing the initial charging time of the electric automobile. In the optimization process, parameters such as iteration times, initial inertia weight, learning factors, population quantity and the like of the improved particle swarm algorithm, and parameters such as charging power of electric vehicles, quantity of electric vehicles, battery capacity, charging efficiency and the like are initialized. Initializing an electric vehicle charging time sequence in a scheduling period according to model parameters and constraint conditions, namely improving an initial population of a particle swarm algorithm, wherein l represents the population of particles and T represents ev_start,l,m And (5) representing the initial charging time of the mth electric automobile of the ith population.
And determining an fitness function formula of the improved particle swarm algorithm by taking the ordered charging objective function as the fitness function of the improved particle swarm algorithm:
y ev ＝λ 1 Q+λ 2 Z
calculating the fitness of each particle based on a fitness function, judging whether to update the pbest (t) and the gbest (t) of the initial particle swarm according to the result, and updating the position of the next generation of particles, namely the position of the charging time of the electric automobile, by combining the following formula, wherein the speed constraint is a positive value; if the latest charging time is greater, the latest charging time point is constrained.
v i (t+1)＝ω*v i (t)+r 1 *c 1 *(pbest(t)-x i (t))+r 2 *c 2 *(gbest(t)-x i (t))
x i (t+1)＝x i (t)+v i (t+1)
In the formula, v i (t) and x i (t) represents the particle speed and position of the electric vehicle number with dimension d at t iterations, the position represents the initial charging time of the electric vehicle, ω represents the inertial weight, r 1 And r 2 A random number of 0 to 1, c 1 And c 2 Representing the learning factor, pbest (t) represents the individual extremum at t iterations, gbest (t) represents the global optimum at t iterations. In the particle updating process, an updating mechanism for adaptively updating inertia weight and learning factors is adopted to adjust the two parameters, so that the conventional particle swarm algorithm is prevented from being trapped into local optimization. And judging whether the optimization process meets the iteration ending condition, if not, continuing the iteration optimization until the iteration ending condition is met, and if so, outputting the ordered charging time distribution of the electric automobile. And outputting the particles which finally meet the conditions, namely the initial charging time sequence, as the ordered charging time of the electric automobile.
The updating mechanism for adaptively updating the inertia weight and the learning factor is as follows:
wherein a is the population of particles, f p (x i (t)) represents the fitness of the ith iteration and the ith particle, namely the size of the ordered charge objective function, and lambda (t) represents the smoothness of the weight change of the ith iteration;
wherein f p Representing the fitness of each iteration particle, namely the size of an ordered charge objective function, f ave Mean value of ordered charge objective function, f, representing fitness of particle swarm min Representing the fitness of the particle swarm, namely the minimum value of the ordered charge objective function, the strategy increases the pertinence of learning factor updating.
Assuming that 1000 electric vehicles are in total in the comprehensive energy system, adopting a 1-day charging mode, the capacity of the electric vehicles is 52kWh of a conventional model, the running 100km consumed electric quantity is 15kWh, and all electric vehicles are scheduled. According to the running characteristics of the electric automobile, the electric automobile is assumed to obey N at the travel end time (17.6,3.4), the electric automobile is assumed to obey N at the first travel time, namely the end charging time (8.92,3.24), the daily driving mileage charging of the electric automobile is obeying the lognormal distribution N (3.2,0.88), and the charging electric quantity expected by a user is 95%. The time-of-use electricity price policy of the region, 10-14 and 19-21 are peak time periods, and the electricity price is 1.26 yuan/kWh; 7-9, 15-18 and 22-23 are flat sections, and the electricity price is 0.70 yuan/kWh; 0-6 is the valley section, and the electricity price is 0.43 yuan/kWh. The method comprises the steps that in an electric vehicle charging scene in which a base load data variance value predicted in the day is larger than a base load variance average value in historical data and meteorological data are displayed as overcast and rainy days, a weight coefficient of user charging cost is 2/6, a weight of power grid load fluctuation degree is 2/6, and a weight of user charging convenience is 2/6 in an optimized objective function. By the above-described sequential charging optimization method, an optimized sequential charging load curve is obtained, as shown in fig. 4.
As can be seen from the graph in fig. 4, when the user carries out disordered charging according to own habits, a large number of electric vehicles are concentrated between 16 and 22 points to charge, and reach a charging peak near 20 points, and the charging peak is very close to the peak period of the base load, and the peak-to-peak peaking increases the load peak of the power system, so that the stable operation of the power system is affected. In addition, peak periods during unordered charging are concentrated on peak electricity prices, which also increases the charging costs for the users.
After the proposed ordered charging method is adopted, the electric vehicle does not adopt a stop-and-charge mode, after the electric vehicle is connected with the charging pile, the electric vehicle waits for the energy management controller to send a control signal and then is connected with the power supply for charging, and the time for sending the signal for charging is calculated by the ordered charging method of the electric vehicle, such as an ordered charging load curve in fig. 4. In the figure, after the charging cost of a user is considered, the charging quantity of the electric automobile in the electricity price peak time is reduced, and the electricity price peak time is also the peak time of the basic load, so that the load fluctuation degree of the power grid is improved. In addition, because the scene is a overcast and rainy day, the charging convenience of users needs to be considered, and the charging mode of partial users according to the habit of the users is met, partial charging load is overlapped with the peak period of the basic load, and compared with the unordered charging mode, the fluctuation degree of the power grid load is improved.
In order to further embody the optimization effect of the ordered charging method adopted in the embodiment on the consideration of the user charging cost, the charging convenience and the power grid load variance, indexes such as charging cost, power grid load peak-valley difference, power grid load variance and charging convenience under unordered charging and ordered charging methods are compared, and the specific is shown in table 1. As can be seen from the results in the table, the load peak-valley difference, the load variance value of the power grid and the user cost are still optimized to a certain extent, and the peak-valley difference is from 12.51kW to 9.80kW, and the variance is 12.86MW 2 To 7.86MW 2 The cost of users is reduced from 23844.2 yuan to 19293.2 yuan by 21.68%, 38.89% and 19.09%, respectively. Rainy weather periodPart of car owners charge according to the habit of the car body, and do not charge according to the strategy completely, so that the charging convenience is 0.67.
TABLE 1
| Type(s) | Peak-valley difference of power grid load (MW) | Power grid load variance (MW) 2 ) | User charging cost (Yuan) | User convenience |
| Base load | 9.82 | 8.48 | / | / |
| Disordered charging | 12.51 | 12.86 | 23844.2 | 0 |
| Ordered charging | 9.80 | 7.86 | 19293.2 | 0.69 |
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the invention in any way, and any person skilled in the art may make modifications or alterations to the disclosed technical content to the equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (10)
1. The comprehensive energy system containing the electric automobile charging is characterized by comprising an energy storage system, an energy storage converter, photovoltaic power generation equipment, a photovoltaic inverter, a charging pile, a load system, an energy management controller and an energy management center; the energy management controller establishes communication connection with the energy storage system, the energy storage converter, the photovoltaic power generation equipment, the photovoltaic inverter, the charging pile and the load system through RS485 communication, and performs information interaction with the energy management center through the Ethernet; the energy management center optimizes the initial charging time of the electric automobile by improving a particle swarm algorithm based on the running state information of each device uploaded by the energy management controller stored in the database as a data base and on the charging cost, the charging convenience and the load fluctuation degree of the power grid of a user, determines the address issued by a signal according to a communication protocol point table according to an optimization result, and accurately issues the address to a charging pile to be controlled by the energy management controller so as to realize ordered charging of the electric automobile.
2. The integrated energy system comprising electric vehicle charging according to claim 1, wherein the energy storage system comprises an energy storage battery and a BMS battery management system, the energy storage battery stores and releases electric energy through charging and discharging, and the output direct current realizes conversion of alternating current and direct current through an energy storage converter; the BMS battery management system collects information of total voltage and total current of the battery pack, highest voltage, lowest voltage and temperature of the single battery in real time, and estimates the state of charge (SOC) and the state of health (SOH) of the battery pack according to the collected information; the controller of the energy storage converter receives a background control instruction through communication and controls the energy storage converter to charge or discharge an energy storage battery in a P/Q mode; the controller of the energy storage converter is communicated with the BMS battery management system through RS485 communication to acquire battery pack state information, realize protective charge and discharge of the battery and ensure battery operation safety.
3. The integrated energy system with electric vehicle charging of claim 1, wherein the charging pile is a unidirectional ac charging pile supporting charging of the electric vehicle at a constant power; the DSP core control board is arranged in the charging pile, so that the state information of the electric automobile connected to the charging pile can be uploaded to the energy management controller, and signals sent by the energy management controller are received to set the charging start time and the charging end time of the charging pile.
4. The integrated energy system with the electric automobile charging function according to claim 1, wherein the energy management controller comprises a plurality of types of input/output interfaces, is connected with an energy storage system, an energy storage converter, photovoltaic power generation equipment, a photovoltaic inverter, a charging pile and a load system through RS485 communication, and is communicated by adopting a standard MODBUS RTU communication protocol; the DSP core control board of each device exchanges information with the energy management controller at regular intervals to realize the monitoring and control of the energy management controller on the running state of the comprehensive energy system; the energy management controller establishes communication with the energy management center through the Ethernet, uploads signals to the energy management center and accurately transmits the signals transmitted by the energy management center.
5. The integrated energy system with electric vehicle charging according to claim 1, wherein the energy management center is connected with the energy management controller through an ethernet, and the data of the energy management controller is read according to IEC60870-104 standard communication and stored in an SQL server database; the energy management center is provided with a software program for realizing an ordered charging method, the ordered charging method is developed based on MATLAB, an ODBC data source is configured in a Windows operating system to be connected with an SQL Server database, and running state information of each device is obtained; and determining an address corresponding to the control signal according to the ordered charging optimization result by the MODBUS protocol point table, accurately issuing the address to a charging pile DSP core control board to be controlled by the energy management controller, and controlling the initial charging time and the end charging time of the charging pile DSP core control board to be controlled.
6. An orderly charging method based on the integrated energy system containing electric vehicle charging according to any one of claims 1 to 5, characterized by comprising the following steps:
step S1: a Monte Carlo method is adopted to establish a charge load typical curve of the electric automobile in a disordered charge state;
step S2: establishing an ordered charging model considering the charging cost, the charging convenience and the power grid load fluctuation degree of a user;
step S3: dynamically weighting the ordered charging objective function according to the base load variance and weather type predicted in the day-ahead;
step S4: and solving an ordered charging objective function meeting constraint conditions by adopting an improved particle swarm algorithm, and optimizing the initial charging time of the electric automobile.
7. The method of ordered charging according to claim 6, wherein the implementation method of step S1 is as follows:
firstly, analyzing the driving characteristics of the electric automobile, wherein the travel end time, the first travel time and the daily driving mileage of the electric automobile meet the following probability density functions:
wherein mu is D 、σ d 、μ e 、σ e Mu and sigma are respectively the expected and standard deviation of the travel mileage, the first travel time and the travel end time; according to the daily driving mileage data and the automobile battery parameters, calculating the initial charge state when the automobile is charged, wherein the calculation formula is as follows:
Wherein i represents the i-th electric vehicle, SOC end,i State of charge, SOC indicating end of charge start,i Representing the state of charge of the initial charge, T c_all,i Indicating the total charge time, w ev Represents the battery power consumed when the electric automobile runs for hundred km, E ev Represents the battery capacity of the electric automobile, lambda ev_eff Charge efficiency for battery, p charge Representing the charging power of the electric automobile;
starting to charge to a disordered charge mode at the end time of the stroke; combining the formal characteristics of the electric automobile and the calculation formula of the charging duration, and adopting a Monte Carlo method to simulate the initial charging time, the daily driving mileage and the first travel time of the electric automobile to obtain a typical curve of the charging load of the electric automobile in a disordered charging state; and adopting a Monte Carlo method to perform multiple simulation to obtain an average value, and obtaining a charging load typical curve of the electric automobile in a disordered charging state.
8. The method according to claim 6, wherein in step S2, the objective function of the charging cost of the user side is:
wherein m represents the charging quantity of the electric automobile, T represents the scheduling period, S (T) represents the charging electricity price of the electric automobile at the moment T, and P i t The charging power of the ith electric automobile at the t moment is shown;
The power grid side takes the load fluctuation degree as an optimization target, and an objective function of the power grid load fluctuation degree is as follows:
wherein P is load_mean Average value P representing sum of system base load and electric vehicle charging load 0 (t) represents the base load at time t, obtained by prediction before the day,representing the charging load of m electric vehicles at time t, V 2 Representing the power grid load variance;
the user charging convenience reflects the personal habit change degree, and the change quantity of the charging electric quantity in each period is measured:
wherein f 0 (t) represents the charging load of the electric vehicle at the time t before the ordered charge control, and f (t) represents the charging load at the time t after the ordered charge control;
taking the difference of dimension and unit of charging cost and power grid load fluctuation degree into consideration, carrying out normalization processing on an objective function and giving a weight coefficient, wherein the formula is as follows:
y ev ＝λ 1 Q+λ 2 Z+λ 3 B
wherein q (t) 0 ) max Representing the maximum charging cost of the electric automobile before orderly control, V 2 max Representing maximum grid load variance, lambda, before ordered charging 1 、λ 2 And lambda (lambda) 3 Weights respectively representing 3 optimization targets, each reflecting the proportion occupied by the charge cost of the user, the fluctuation degree of the power grid load and the charge convenience of the user in the optimization model, lambda 1 、λ 2 And lambda (lambda) 3 The sum of (2) is 1;
Constraint conditions of the ordered charging model are as follows:
the initial charging time constraint of the electric automobile; when the initial charging of the electric automobile is optimized, the charging time of the automobile owner is required to be after the travel time of the electric automobile is finished and before the first trip time of the next day, and the charge state of the automobile owner needs to be ensured to meet the requirement of the automobile owner, so the initial charging time t of the electric automobile is required start,i The following constraints are satisfied:
wherein i represents the i-th electric vehicle, SOC ref,i Indicating that the vehicle owner expects to reach the charge state, t need,i Indicating the charging time, t end,i The time representing the first trip is the latest charging time, t start_inial,i Indicating the stroke end time;
continuous constraint of the battery charge state of the electric automobile; in the charging process of the electric automobile battery, the SOC update meets the following constraint:
in SOC t+1,i Representing the state of charge at time t+1, SOC t,i Representing the state of charge at time T, T C,t,i The charging time at the time t is represented;
upper limit constraint of power grid load; the sum of the base load and the charging load of the electric automobile in the corresponding region cannot exceed the maximum capacity of the transformer.
9. The method according to claim 6, wherein the step S3 is implemented by:
according to the average value comparison result of the daily predicted base load variance and the base load variance in the historical data, combining the predicted weather types, constructing the following four scenes and respectively setting the weight distribution values of the objective function:
Scene 1: the base load data variance value predicted before the day is larger than the base load variance average value in the historical data, and the meteorological data is displayed as a sunny day;
scene 2: the base load data variance value predicted before the day is larger than the base load variance average value in the historical data, and the meteorological data are displayed as overcast and rainy days;
scene 3: the base load data variance value predicted before the day is smaller than the base load variance average value in the historical data, and the weather data report result is a sunny day;
scene 4: the base load data variance value predicted before the day is smaller than the base load variance average value in the historical data, and the meteorological data are displayed as overcast and rainy days;
according to the scene, when the variance of the daily predicted base load data is larger, the improvement of the load fluctuation degree of the power grid is more focused, and the lambda is improved 2 A numerical value; when weather data shows that weather is overcast and rainy days, users pay more attention to convenience, and more users adopt instant stop and instant fillDisordered charging mode, improving lambda 3 A numerical value; user cost minimization is an important factor in the optimization process, is also a key for exciting users to participate in ordered charging, and has higher weight under different charging scenes.
10. The method according to claim 6, wherein the step S4 is implemented by:
Initializing an improved particle swarm algorithm and parameters of the electric automobile; initializing an initial charging time sequence of the electric automobile group to be a particle group initial position according to model parameters, constraint conditions and a disordered charging load typical curve, and taking an ordered charging objective function as an adaptation function of an improved particle group algorithm; the position of the next generation particle, i.e. the charging time distribution, is updated in combination with the following particle formula:
v i (t+1)＝ω*v i (t)+r 1 *c 1 *(pbest(t)-x i (t))+r 2 *c 2 *(gbest(t)-x i (t))
x i (t+1)＝x i (t)+v i (t+1)
in the particle updating process, an updating mechanism for adaptively updating inertia weight and learning factors is adopted to adjust the two parameters, so that the conventional particle swarm algorithm is prevented from being trapped into local optimization; outputting the particles which finally meet the conditions, namely the initial charging time sequence, as the ordered charging time of the electric automobile;
the updating mechanism for adaptively updating the inertia weight and the learning factor is as follows:
wherein a is the population of particles, f p (x i (t)) represents the fitness of the ith iteration, i.e. the size of the ordered charge objective function,λ (t) represents the smoothness of the variation of the weight of the t-th iteration;
wherein f p Representing the fitness of each iteration particle, namely the size of an ordered charge objective function, f ave Mean value of ordered charge objective function, f, representing fitness of particle swarm min Representing the fitness of the particle swarm, namely the minimum value of the ordered charge objective function, the strategy increases the pertinence of learning factor updating.
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