CN114583729A - Light-storage electric vehicle charging station scheduling method considering full-life-cycle carbon emission - Google Patents
Light-storage electric vehicle charging station scheduling method considering full-life-cycle carbon emission Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a light-storage electric vehicle charging station scheduling method considering full-life-cycle carbon emission. And secondly, establishing a charging station optimization scheduling model considering the carbon emission of the full life cycle and the operation cost of the charging station by taking the charge-discharge state of each EV accessed to the power grid and the charge-discharge state of the energy storage equipment as optimization variables. And solving to obtain a charging station day-ahead scheduling plan by using an improved non-dominated sorting genetic algorithm. The PV and the energy storage equipment are preferentially used for supplying power to the EV in the operation process of the charging station, when the PV and the energy storage equipment cannot meet the EV charging requirement, the charging station purchases power from the power grid to ensure the EV charging requirement, and finally data are collected to correct the original model.
Description
Technical Field
The invention relates to an electric automobile charging and discharging service technology, in particular to a photovoltaic electric automobile charging station scheduling method considering full life cycle carbon emission.
Background
Although Electric Vehicles (EVs) do not generate carbon emissions during operation, power generation structures in our country are still mainly thermal power generation, and the increasing demand for charging generated by the power generation structures can cause power generation systems to generate a large amount of carbon emissions. In addition, the carbon emission is not generated in the power generation process of new energy sources such as photovoltaic power generation, wind power generation and the like, but the carbon emission is generated in the production, manufacturing and recycling processes of equipment. Aiming at the problem, a light-storage EV charging station scheduling method considering the carbon emission in the full life cycle is provided. The traditional dispatching method generally takes price as guidance to guide a user to charge in a time period with lower electricity price, so that the cost of the user and a charging station is reduced as much as possible. After considering the problem of carbon emission, the charging station preferentially uses a charging station Photovoltaic (PV) and an energy storage device to charge the EV under the condition of meeting the charging requirement of an EV user, and the energy storage device of the charging station charges in a time period with a higher new energy power generation occupation of the system, so that the new energy power generation consumption is promoted. Since the time period in which the new energy power generation ratio is high is not necessarily the time period in which the electricity price is low, the charging station loses a part of the benefit to promote the new energy consumption and reduce the carbon emission.
Disclosure of Invention
The invention provides an optimal scheduling method comprehensively considering carbon emission and charging station operation cost, and mainly aims to reduce the carbon emission indirectly generated in the EV operation process.
According to the invention, the charging station can acquire the charging requirement of the user, the user receives the charging and discharging scheduling of the charging station, and the charging station can acquire the new energy power generation ratio data at each moment when the power grid operates from the power grid. Firstly, according to historical data of the new energy power generation ratio at each moment provided by a power grid, historical photovoltaic power generation data and weather forecast data of a charging station respectively predict the thermal power generation ratio and photovoltaic output in the future day. And secondly, establishing a charging station optimization scheduling model considering the carbon emission of the full life cycle and the operation cost of the charging station by taking the charge-discharge state of each EV accessed to the power grid and the charge-discharge state of the energy storage equipment as optimization variables. And solving to obtain a charging station day-ahead scheduling plan by using an improved non-dominated sorting genetic algorithm. The PV and the energy storage equipment are preferentially used for supplying power to the EV in the operation process of the charging station, and when the PV and the energy storage equipment cannot meet the EV charging requirement, the charging station purchases power from the power grid to ensure the EV charging requirement. The energy storage equipment is charged preferentially in the time period when the new energy power generation of the power grid occupies a higher percentage. And the charging station collects EV charging data, PV output data and new energy ratio data at each moment in the operation process, adds the data into a corresponding data set, and corrects the original model.
The method is implemented according to the following steps:
step 1, predicting the power generation proportion of new energy;
step 2, PV output prediction of a charging station;
step 4, constructing a scheduling strategy with the minimum carbon emission as a target;
the life cycle of the equipment is mainly divided into three stages of production, use and scrapping, carbon emission is possibly generated in the three processes, no carbon emission exists in the operation process of the PV equipment and the energy storage equipment, and the carbon emission is mainly generated in the production, construction and recycling processes. The N minute of carbon emission in the production and recovery process of equipment based on the cost equal-year-value method is shown as the formula (1)[1]。
Wherein L is0The carbon emissions per N minutes turned off; i is1The discount rate is obtained; m is1The service life of the equipment; psimadeCarbon emission in the production process; psireIs carbon emission in the recovery process.
Carbon emission is generated in the production, operation and scrapping recovery stages of EV and traditional fuel automobiles, and the invention takes the reduction of carbon emission in the operation process of a charging station as a research target, so that the carbon emission of the automobiles in the production and scrapping recovery stages is not considered. Since the permeability of the EV is directly related to the charging load, the carbon emissions generated during the operation of the fuel vehicle are considered, and the specific calculation is shown in equation (2):
Lv＝γ·AGC·M (2)
wherein L isvCarbon emission generated in the running process of the fuel vehicle; gamma is a carbon emission factor of the fuel vehicle; AGC is unit mileage oil consumption; and M is the driving mileage.
Due to the limited PV capacity of the charging stations and the greater variability of PV exposure to sunlight, charging stations also need to purchase power from a large grid to meet EV customer charging demands in the event of insufficient PV supply. The carbon emission of the charging station in the operation process is specifically shown as the formula (3).
Wherein L is carbon emission generated when the charging station operates; l isfixThe specific calculation is shown as a formula (4) for the conversion value of the carbon emission of the whole life cycle of the charging station equipment; m represents the number of fuel vehicles, Lv,iRepresents the carbon emissions of the ith fuel vehicle; alpha represents the carbon emission intensity of the thermal power unit for power generation; beta is a betatRepresenting the proportion of thermal power in the power generation system at the moment t, and assuming that the value of the proportion is available; pbuyIndicating that the electricity is purchased to a large power grid; ppvRepresents the PV output of the charging station; pbatIndicating the output of the energy storage system of the charging station; pV2GIndicating EV user engagement with the discharge power of V2G.
Wherein, Ppv,maxPV maximum installed capacity; pbat,maxThe maximum capacity of the energy storage device; psipv、ψbatIs the unit capacity PV, the full life cycle carbon emission of the energy storage equipment; n is the number of charging piles; psipvCarbon emission for a full life cycle of a charging pile; psicsCarbon emission is generated in the process of building, dismantling and recovering the charging station foundation;
step 5, a dispatching strategy taking the lowest operation cost of the charging station as a target;
the charging station operation cost is specifically expressed as shown in formula (5).
F＝c1Ppv+c2Pbat+cbPg,b-csPg,s+cvPev,d-c3Pev,c (5)
Wherein, c1，c2Respectively the unit power output cost of the PV and the energy storage equipment; c. Cb、csThe electricity price for purchasing and selling electricity to the power grid for the charging station; c. CvSubsidizing the cost of the user participating in V2G for the charging station; c. C3For selling electricity at a charging station, Pg,bIndicating that the charging station is purchasing power from the grid, Pg,sRepresenting the amount of electricity sold by the power grid to the charging station; p isev,cCharging power for the EV; pev,dThe power is discharged for EV participation V2G.
Step 6, construction of scheduling policy constraint conditions
(1) Power constraint
Ppv+Pg+Pbat＝Pev,c-Pev,d (6)
Wherein, PpvFor charging station PV contribution, PgThe value of the power exchange between the charging station and the power grid is positive when the charging station purchases power, and the value of the power exchange between the charging station and the power grid is negative when the charging station feeds power to the power grid; pbatThe power of the energy storage equipment is charged in a charging station, the value of the energy storage equipment is positive when the energy storage equipment is discharged, and the value of the energy storage equipment is negative when the energy storage equipment is charged; pev,cCharging power for the EV; pev,dThe power is discharged for EV participation V2G.
(2) Energy storage device restraint
Smin≤St≤Smax (7)
St＝St-1+(ηbat,cλbat,cPbat,c-λbat,dPbat,d/ηbat,d)Δt (8)
λbat,c·λbat,d＝0,λbat,c,λbat,d∈{0,1} (9)
Wherein S istThe electric quantity of the energy storage equipment at the moment t; sminIs the minimum value of the electric quantity of the energy storage equipment, SmaxIs the maximum value of the electric quantity of the energy storage equipment, etabat,cCharging efficiency for the energy storage device; pbat,cEnergy storage device charging power; etabat,dDischarging efficiency for the energy storage device; p isbat,dThe discharge power of the energy storage device; Δ t is the interval time; lambda [ alpha ]bat,c，λbat,dFor the charge-discharge state quantity, lambda when the energy storage device is in the charging statebat,cIs 1, λbat,dIs 0; in the discharge state, λbat,dIs 1; lambda [ alpha ]bat,cIs 0.
(3) EV battery restraint
λev,c·λev,d＝0,λev,c,λev,d∈{0,1} (12)
Wherein,
EV electric quantity at t moment; ecapIs the EV battery capacity; eminThe electric quantity of the EV battery is the minimum value; the minimum electric quantity when the EV accesses the charging station; the electric quantity when the EV leaves the charging station; emaxIs the maximum value of the electric quantity of the EV battery, etaev,cCharging efficiency for the energy storage device; pev,cEnergy storage device charging power; etaev,dDischarging efficiency for the energy storage device; p isev,dThe discharge power of the energy storage equipment; Δ t is the interval time; lambda [ alpha ]ev,c，λev,dFor the charge-discharge state quantity, lambda when the energy storage device is in the charging stateev,cIs a number of 1, and the number of the main chain is 1,λev,dis 0; in the discharge state, λev,dIs 1; lambda [ alpha ]ev,cIs 0.
Step 7, scheduling strategy solving method
Using a base d for the above-mentioned multiple objective problem2The distance (Euclidean distance from a reference vector) improved non-dominated sorting genetic algorithm (NSGA-II) is used for solving, firstly, the population is initialized, then the corresponding objective function value of the population is calculated, the non-dominated sorting is carried out on the objective function value, and the objective function value is based on d2And (4) selecting non-dominant layer individuals of the distance, generating an offspring population through selection, crossing and variation, combining the parent population and the offspring population to obtain a new population, and repeating the operation until a termination condition is met. According to the method, the influence of the EV permeability, the proportion of the user participating in V2G and the proportion of renewable energy in the power grid on the result can be further researched, so that a certain reference effect on energy conservation and emission reduction is achieved.
Step 8, model correction
And (3) in the actual operation process, the charging station collects EV charging data, PV output data, weather data of the same day and new energy power generation ratio data of each moment of the power grid, adds the data into a corresponding data set, and corrects the prediction models in the steps 1 and 2 to further enable the models to be more accurate.
Preferably, the new energy power generation ratio is predicted; the method specifically comprises the following steps:
because the new energy power generation ratio at each moment is a time sequence, a long-time memory network with good processing capacity on the time sequence is selected for prediction, wherein the LSTM has 3 hidden layers, each layer has 300 LSTM unit networks, and the hidden layers use a ReLU function as an activation function; the network inputs the new energy power generation ratio data of each time of the previous 30 days, and outputs the new energy power generation ratio data of each time of the previous day.
Preferably, the charging station PV output prediction is in particular
And (3) predicting the output of the photovoltaic cell model due to the lack of PV historical data at the initial stage of the construction of the charging station according to the photovoltaic cell model, wherein the photovoltaic cell model is specifically shown as a formula (13).
Wherein I is the output current of the photovoltaic cell; u is the output voltage of the photovoltaic cell; s is the illumination intensity and is 1000W/m under the standard condition2(ii) a T is the surface temperature of the battery; t isrefIs a reference temperature under standard conditions; isc is the short-circuit current of the photovoltaic cell; i is0The current is reversely saturated by the diode; q is the charge capacity; n is the diode emission coefficient; k is Boltzmann constant; rsh is the equivalent resistance inside the battery;
in the operation process of the charging station, PV data are more and more sufficient, the method for predicting PV output by using a deep learning method is considered, the PV output is greatly influenced by weather factors and has strong time correlation, and the LSTM is used for predicting PV output. The LSTM has 3 hidden layers, each layer has 300 LSTM unit networks, and the hidden layers use the ReLU function as the activation function. The network selects irradiance, temperature, humidity, scattering degree and PV output historical data of each moment in the previous seven days, which have large influence on the network, as input. The output of the network is the PV output situation at each time of the future day.
Preferably, a charge-discharge model of the EV and the energy storage equipment is constructed; the method specifically comprises the following steps:
the EV and energy storage device charge-discharge model is represented by equation (14):
wherein,
the state of charge of the EV or the energy storage equipment at the moment t; pt cAnd Pt dRated charging power and discharging power of the EV or the energy storage equipment at the moment t are respectively; e is the EV battery capacity or the energy storage equipment capacity; etac,ηdCharging and discharging efficiencies of the EV or the energy storage equipment respectively; Δ t is the time interval.
The invention has the advantages and beneficial results that:
(1) the invention comprehensively considers the carbon emission and the charging station operation cost in the running process of the EV charging station.
(2) The invention can further research the influence of the main influence factors on the result and provides more comprehensive reference value for reducing carbon emission.
(3) Base d used in the invention2The distance improved NSGA-II algorithm can improve sample diversity and further improve global search capability.
(4) The invention continuously updates and perfects the corresponding data set, and corrects the model, so that the accuracy of the model can be further improved in the operation process.
Drawings
FIG. 1 shows a new energy power generation ratio prediction LSTM structure diagram;
FIG. 2 is based on d2Distance improved NSGA-II algorithm flow.
Detailed Description
Step 1, predicting the power generation proportion of new energy;
as shown in fig. 1, because the new energy power generation ratio at each moment is a time sequence, a long-time memory network with good processing capability for the time sequence is selected to predict, wherein the LSTM has 3 hidden layers, each layer has 300 LSTM unit networks, and the hidden layers use a ReLU function as an activation function; the network inputs the new energy power generation ratio data of each time in the previous 30 days, and outputs the new energy power generation ratio data of each time in the next day.
Step 2, PV output prediction of a charging station;
due to the lack of PV historical data in the initial stage of charging station construction, the output of the photovoltaic cell model is predicted according to the photovoltaic cell model, and the photovoltaic cell model is specifically shown as a formula (1).
Wherein I is the output current of the photovoltaic cell; u is the output voltage of the photovoltaic cell; s is the illumination intensity and is 1000W under the standard condition/m2(ii) a T is the surface temperature of the battery; t isrefIs a reference temperature under standard conditions; isc is the short-circuit current of the photovoltaic cell; i is0Reverse saturation current for the diode; q is the charge capacity; n is the diode discharge coefficient; k is Boltzmann constant; rsh is the equivalent resistance inside the battery;
in the operation process of the charging station, PV data are more and more sufficient, the method for predicting PV output by using a deep learning method is considered, the PV output is greatly influenced by weather factors and has strong time correlation, and the LSTM is used for predicting PV output. The LSTM has 3 hidden layers, each layer has 300 LSTM unit networks, and the hidden layers use the ReLU function as the activation function. The network selects irradiance, temperature, humidity, scattering degree and PV output historical data of each moment in the previous seven days, which have large influence on the network, as input. The output of the network is the PV output situation at each time of the future day.
the EV and energy storage device charge-discharge model is represented by equation (2):
wherein,
the state of charge of the EV or the energy storage equipment at the moment t; pt cAnd Pt dRated charging power and discharging power of the EV or the energy storage equipment at the moment t respectively; e is the EV battery capacity or the energy storage equipment capacity; etac,ηdCharging and discharging efficiencies of the EV or the energy storage equipment respectively; Δ t is the time interval.
Step 4, constructing a scheduling strategy with the minimum carbon emission as a target;
the life cycle of the equipment is mainly divided into three stages of production, use and scrapping, carbon emission is possibly generated in the three processes, no carbon emission exists in the operation process of the PV equipment and the energy storage equipment, and the carbon emission is mainly generated in the production, construction and recycling processes. The N minutes of carbon emission in the production and recovery process of equipment based on the cost equal-year-number method is reflected as the formula (3) [1 ].
Wherein L is0The carbon emissions per N minutes turned off; i is1The discount rate is obtained; m is1The service life of the equipment; psimadeCarbon emission in the production process; psireIs carbon emission in the recovery process.
Carbon emission is generated in production, operation and scrapping recovery stages of EV and traditional fuel oil automobiles, and the carbon emission of the automobiles in the production and scrapping recovery stages is not considered by taking the reduction of the carbon emission in the operation process of a charging station as a research target. Since the permeability of EV is directly related to the charging load, considering the carbon emissions generated during the operation of the fuel vehicle, the specific calculation is as shown in equation (4):
Lv＝γ·AGC·M (4)
wherein L isvCarbon emission generated in the running process of the fuel vehicle; gamma is a carbon emission factor of the fuel vehicle; AGC is unit mileage oil consumption; and M is the driving mileage.
Due to the limited PV capacity of the charging stations and the greater variability of PV exposure to sunlight, charging stations also need to purchase power from a large grid to meet EV customer charging demands in the event of insufficient PV supply. The carbon emission of the charging station in the operation process is specifically shown in the formula (5).
Wherein L is carbon emission generated when the charging station operates; l isfixThe specific calculation is shown as a formula (6) for the conversion value of the carbon emission of the whole life cycle of the charging station equipment; m represents the number of fuel vehicles, Lv,iRepresents the carbon emissions of the ith fuel vehicle; alpha represents the carbon emission intensity of the thermal power unit for power generation; beta is atRepresenting the proportion of thermal power in the power generation system at the moment t, and assuming that the value of the proportion is available; pbuyIndicating that the electricity is purchased to a large power grid; p ispvRepresents the PV output of the charging station; pbatIndicating the output of the energy storage system of the charging station; pV2GIndicating EV user engagement with the discharge power of V2G.
Wherein, Ppv,maxPV maximum installed capacity; pbat,maxThe maximum capacity of the energy storage device; psipv、ψbatIs the unit capacity PV, the full life cycle carbon emission of the energy storage equipment; n is the number of charging piles; psipvCarbon emission for a full life cycle of a charging pile; psicsCarbon emission is generated in the process of building, dismantling and recovering the charging station foundation;
step 5, a scheduling strategy with the lowest operation cost of the charging station as a target;
the charging station operation cost is specifically expressed as shown in formula (7).
F＝c1Ppv+c2Pbat+cbPg,b-csPg,s+cvPev,d-c3Pev,c (7)
Wherein, c1，c2Respectively the unit power output cost of the PV and the energy storage equipment; c. Cb、csThe electricity price for purchasing and selling electricity to the power grid for the charging station; c. CvSubsidizing the cost of the user participating in V2G for the charging station; c. C3For selling electricity at a charging station, Pg,bIndicating that the charging station is purchasing power from the grid, Pg,sRepresenting the amount of electricity sold by the power grid to the charging station; pev,cCharging power for the EV; pev,dThe power is discharged for EV participation V2G.
Step 6, construction of scheduling policy constraint conditions
(1) Power constraint
Ppv+Pg+Pbat＝Pev,c-Pev,d (8)
Wherein, PpvFor charging station PV contribution, PgThe value of the power exchange between the charging station and the power grid is positive when the charging station purchases power, and the value of the power exchange between the charging station and the power grid is negative when the charging station feeds power to the power grid; pbatThe power of the energy storage equipment of the charging station is determined, the value of the energy storage equipment is positive when the energy storage equipment is discharged, and the value of the energy storage equipment is negative when the energy storage equipment is charged; pev,cCharging power for the EV; pev,dThe power is discharged for EV participation V2G.
(2) Energy storage device restraint
Smin≤St≤Smax (9)
St＝St-1+(ηbat,cλbat,cPbat,c-λbat,dPbat,d/ηbat,d)Δt (10)
λbat,c·λbat,d＝0,λbat,c,λbat,d∈{0,1} (11)
Wherein S istThe electric quantity of the energy storage equipment at the moment t; sminIs the minimum value of the electric quantity of the energy storage equipment, SmaxIs the maximum value of the electric quantity of the energy storage equipment etabat,cCharging efficiency for the energy storage device; pbat,cEnergy storage device charging power; etabat,dDischarging efficiency for the energy storage device; pbat,dThe discharge power of the energy storage device; Δ t is the interval time; lambda [ alpha ]bat,c，λbat,dFor the charge-discharge state quantity, lambda when the energy storage device is in the charging statebat,cIs 1, λbat,dIs 0; in the discharge state, λbat,dIs 1; lambda [ alpha ]bat,cIs 0.
(3) EV battery restraint
λev,c·λev,d＝0,λev,c,λev,d∈{0,1} (14)
Wherein,
EV electric quantity at t moment; ecapIs the EV battery capacity; eminThe electric quantity of the EV battery is the minimum value; the minimum electric quantity when the EV accesses the charging station; the electric quantity when the EV leaves the charging station; emaxIs the maximum value of the electric quantity of the EV battery, etaev,cCharging efficiency for the energy storage device; pev,cEnergy storage device charging power; etaev,dDischarging efficiency for the energy storage device; pev,dThe discharge power of the energy storage equipment; Δ t is the interval time; lambda [ alpha ]ev,c，λev,dFor the charge-discharge state quantity, lambda when the energy storage device is in the charging stateev,cIs 1, λev,dIs 0; in the discharge state, λev,dIs 1; lambda [ alpha ]ev,cIs 0.
Step 7, scheduling strategy solving method
Using a base d for the above-mentioned multiple objective problems2The distance (Euclidean distance from a reference vector) improved non-dominated sorting genetic algorithm (NSGA-II) is used for solving, firstly, the population is initialized, then the corresponding objective function value of the population is calculated, the non-dominated sorting is carried out on the objective function value, and the objective function value is based on d2And (4) selecting the non-dominant layer individuals of the distance, generating an offspring population through selection, crossing and variation, combining the parent population and the offspring population to obtain a new population, and repeating the operation until a termination condition is met. Based on d2The specific scheme of the distance-modified NSGA-II is shown in FIG. 2. According to the method, the influence of the EV permeability, the proportion of the user participating in V2G and the proportion of renewable energy in the power grid on the result can be further researched, so that a certain reference effect on energy conservation and emission reduction is achieved.
Step 8, model correction
And (3) in the actual operation process, the charging station collects EV charging data, PV output data, weather data of the same day and new energy power generation ratio data of each moment of the power grid, adds the data into a corresponding data set, and corrects the prediction models in the steps 1 and 2 to further enable the models to be more accurate.
Reference to the literature
[1] The electric power conversion equipment and the photovoltaic combined optimization configuration (J) of the comprehensive energy system whole life cycle carbon emission and carbon transaction [ electric power automation equipment (2021, 41(09): 156-). 163 ].
Claims (4)
1. The light-storage electric vehicle charging station scheduling method considering the full-life-cycle carbon emission is characterized by comprising the following steps:
step 1, predicting the power generation proportion of new energy;
step 2, PV output prediction of a charging station;
step 3, constructing charging and discharging models of the EV and the energy storage equipment;
step 4, constructing a scheduling strategy taking minimum carbon emission as a target;
the N minutes of carbon emission in the production and recovery process of equipment based on the cost equal-year-value method is represented by the formula (3);
wherein L is0The carbon emissions per N minutes turned off; i is1The discount rate is obtained; m is a unit of1The service life of the equipment; psimadeCarbon emission in the production process; psireCarbon emission in the recovery process;
since the permeability of EV is directly related to the charging load, considering the carbon emissions generated during the operation of the fuel vehicle, the specific calculation is as shown in equation (4):
Lv＝γ·AGC·M (2)
wherein L isvCarbon emission generated in the running process of the fuel vehicle; gamma is a carbon emission factor of the fuel vehicle; AGC is unit mileage oil consumption; m is the driving mileage;
due to the limited PV capacity of the charging station and the large fluctuation of PV due to sunlight, the charging station needs to purchase power from the large power grid to meet the charging demand of EV users in case of insufficient PV supply; the carbon emission of the charging station in the operation process is specifically shown as a formula (5);
wherein L is carbon emission generated when the charging station operates; l isfixThe specific calculation of the conversion value of the carbon emission of the whole life cycle of the charging station equipment is shown as the formula (6); m represents the number of fuel vehicles, Lv,iRepresents the carbon emissions of the ith fuel vehicle; alpha represents the carbon emission intensity of the thermal power unit for power generation; beta is atRepresenting the proportion of thermal power in the power generation system at the time t; pbuyIndicating that the electricity is purchased to the large power grid; ppvRepresents the PV output of the charging station; pbatRepresenting the output of the energy storage system of the charging station; p isV2GDischarge power representing EV user participation V2G;
wherein, Ppv,maxPV maximum installed capacity; pbat,maxMaximum capacity for energy storage devices; psipv、ψbatIs the unit capacity PV, the full life cycle carbon emission of the energy storage equipment; n is the number of charging piles; psipvCarbon emission for a full life cycle of a charging pile; psicsCarbon emission is generated in the process of building, dismantling and recycling the charging station foundation;
step 5, a scheduling strategy with the lowest operation cost of the charging station as a target;
the operation cost of the charging station is specifically represented as formula (7);
F＝c1Ppv+c2Pbat+cbPg,b-csPg,s+cvPev,d-c3Pev,c (5)
wherein, c1，c2Respectively the unit power output cost of the PV and the energy storage equipment; c. Cb、csPurchase to the power grid for charging stationsElectricity, electricity prices for selling electricity; c. CvSubsidizing the cost of the user participating in V2G for the charging station; c. C3For selling electricity at a charging station, Pg,bIndicate charging station to purchase electric quantity, P, to the electric networkg,sRepresenting the amount of electricity sold by the power grid to the charging station; pev,cCharging power for the EV; pev,dParticipating in V2G discharge power for the EV;
step 6, construction of scheduling policy constraint conditions
(1) Power constraint
Ppv+Pg+Pbat＝Pev,c-Pev,d (6)
Wherein, PpvFor charging station PV contribution, PgThe value of the power exchange between the charging station and the power grid is positive when the charging station purchases power, and the value of the power exchange between the charging station and the power grid is negative when the charging station feeds power to the power grid; pbatThe power of the energy storage equipment of the charging station is determined, the value of the energy storage equipment is positive when the energy storage equipment is discharged, and the value of the energy storage equipment is negative when the energy storage equipment is charged; pev,cCharging power for the EV; pev,dParticipating in V2G discharge power for EV;
(2) energy storage device restraint
Smin≤St≤Smax (7)
St＝St-1+(ηbat,cλbat,cPbat,c-λbat,dPbat,d/ηbat,d)Δt (8)
λbat,c·λbat,d＝0,λbat,c,λbat,d∈{0,1} (9)
Wherein S istThe electric quantity of the energy storage equipment at the moment t; sminIs the minimum value of the energy storage device, SmaxIs the maximum value of the electric quantity of the energy storage equipment, etabat,cCharging efficiency for the energy storage device; pbat,cEnergy storage device charging power; etabat,dDischarging efficiency for the energy storage device; pbat,dThe discharge power of the energy storage device; Δ t is the interval time; lambda [ alpha ]bat,c，λbat,dFor the charge-discharge state quantity, lambda when the energy storage device is in the charging statebat,cIs 1, λbat,dIs 0; in the discharge state, λbat,dIs 1; lambdabat,cIs 0;
(3) EV battery restraint
λev,c·λev,d＝0,λev,c,λev,d∈{0,1} (12)
Wherein,
EV electric quantity at t moment; ecapIs the EV battery capacity; eminThe electric quantity of the EV battery is the minimum value; minimum electric quantity when the EV is accessed into a charging station; the electric quantity when the EV leaves the charging station; emaxMaximum charge of EV battery, etaev,cCharging efficiency for the energy storage device; pev,cEnergy storage device charging power; etaev,dDischarging efficiency for the energy storage device; pev,dThe discharge power of the energy storage device; Δ t is the interval time; lambda [ alpha ]ev,c，λev,dFor charging and discharging quantities, lambda when the energy storage device is in the charging stateev,cIs 1, λev,dIs 0; in the discharge state, λev,dIs 1; lambda [ alpha ]ev,cIs 0;
step 7, scheduling strategy solving method
Using a base d for the above-mentioned multiple objective problems2Solving a distance improved non-dominated sorting genetic algorithm, firstly initializing a population, then calculating a corresponding objective function value of the population, and carrying out non-dominated sorting on the objective function value and based on d2Selecting non-dominant layer individuals of the distance, generating offspring populations through selection, crossing and variation, combining the parent population and the offspring population to obtain a new population, and repeating the operation until a termination condition is met;
step 8, model correction
In the actual operation process, the charging station collects EV charging data, PV output data, weather data of the same day and new energy power generation ratio data of the power grid at all times, adds the data into a corresponding data set, modifies the prediction models in the step 1 and the step 2, and further enables the models to be more accurate.
2. The method of claim 1, wherein the method comprises: predicting the power generation proportion of the new energy; the method specifically comprises the following steps:
because the new energy power generation ratio at each moment is a time sequence, a long-time memory network with good processing capacity for the time sequence is selected to predict, wherein the LSTM has 3 hidden layers, each layer has 300 LSTM unit networks, and the hidden layers use a ReLU function as an activation function; the network inputs the new energy power generation ratio data of each time in the previous 30 days, and outputs the new energy power generation ratio data of each time in the next day.
3. The method of claim 1, wherein the method comprises: charging station PV output prediction
Predicting the output of the photovoltaic cell model according to the photovoltaic cell model, wherein the photovoltaic cell model is specifically shown as a formula (1);
wherein I is the output current of the photovoltaic cell; u is the output voltage of the photovoltaic cell; s is the illumination intensity and is 1000W/m under the standard condition2(ii) a T is the surface temperature of the battery; t isrefIs a reference temperature under standard conditions; isc is the short-circuit current of the photovoltaic cell; i is0Is a diode reverse saturation current; q is the charge capacity; n is the diode emission coefficient; k is Boltzmann constant; rsh is the battery internal equivalent resistance;
in the operation process of the charging station, PV data are more and more sufficient, the PV output is predicted by using an LSTM (least squares maximum Transmission technology) in consideration of the fact that the PV output is greatly influenced by weather factors and has strong time correlation by using a deep learning method, and the PV output is predicted by using the LSTM; the LSTM is provided with 3 hidden layers, each layer is provided with 300 LSTM unit networks, and the hidden layers use a ReLU function as an activation function; the network selects irradiance, temperature, humidity and scattering degree which have great influence on the network and PV output historical data of each moment in the previous seven days as input; the output of the network is the PV output situation at each time of the future day.
4. The method of claim 1, wherein the method comprises: establishing a charge and discharge model of the EV and the energy storage equipment; the method comprises the following specific steps:
the EV and energy storage device charge-discharge model is represented by equation (2):
wherein,
the state of charge of the EV or the energy storage equipment at the moment t; pt cAnd Pt dRated charging power and discharging power of the EV or the energy storage equipment at the moment t are respectively; e is the EV battery capacity or the energy storage equipment capacity; etac,ηdCorrespondingly charging and discharging efficiencies of the EV or the energy storage equipment respectively; Δ t is the time interval.
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