CN111369742B - Method and system for calculating matching benefit of shared parking space and electric vehicle in electric power market - Google Patents

Method and system for calculating matching benefit of shared parking space and electric vehicle in electric power market Download PDF

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CN111369742B
CN111369742B CN202010172968.8A CN202010172968A CN111369742B CN 111369742 B CN111369742 B CN 111369742B CN 202010172968 A CN202010172968 A CN 202010172968A CN 111369742 B CN111369742 B CN 111369742B
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康家熙
樊弈良
汤瑞欣
陈斗
许方园
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Nanjing Runbei Intelligent Environment Research Institute Co ltd
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Abstract

The invention relates to a benefit calculation method and a benefit calculation system of a multi-parking-lot shared parking space and electric vehicle matching system in an electric power market, which comprise a vehicle information module to be evaluated, an improved Laplace multiplier method module, an automobile charging scheme module, a vehicle income prediction module and a gradient optimization algorithm module; the vehicle information module to be evaluated is used for selecting vehicles needing calculating benefits and outputting the vehicle information to the improved Laplace multiplier module; the improved Laplace multiplier method module receives the electricity price information input by the electricity price module, performs power optimization on a vehicle to be evaluated, outputs the optimized vehicle income to the estimated vehicle income module, and simultaneously inputs all possible charging and discharging strategies of the vehicle into the automobile charging scheme module; and the gradient optimization algorithm module carries out secondary optimization on the vehicle according to the received vehicle income information, the charging scheme information and the load information to obtain the benefit information of the vehicle on the parking lot.

Description

Method and system for calculating matching benefit of shared parking space and electric vehicle in electric power market
Technical Field
The invention relates to the field of shared parking spaces, in particular to a benefit calculation method and system of a multi-parking-lot shared parking space and electric vehicle matching system in an electric power market.
Background
With the rapid increase of the holding capacity of electric automobiles, the economy of shared parking spaces based on the electric power market is brought forward. The sharing economy is an important means for improving social efficiency, and the sharing parking stall is an implementation mode of the sharing economy. The owner of the parking space can obtain the income by sharing the parking space to the demander when the parking space is idle. Similarly, the owner of the electric automobile can obtain the income by participating in the electric market through the electric automobile. Generally, a car owner sharing a parking space gives out a time period and an hourly rent for renting the parking space, and an electric car gives out a required parking time and an hourly quotation, and the time period and the hourly rent are matched. However, for the matching problem of the electric vehicle and the shared parking space, in addition to the above time requirement and price requirement, the problem of how the electric vehicle participates in the power market is also considered, and when a plurality of shared electric vehicle parking lots exist, competition among the parking lots and different benefits brought by power price difference among the parking lots are also considered. The behavior that the electric vehicle participates in the electric power market is v2g, namely the electric energy of the electric vehicle is output to the power grid, the electric vehicle can sell electricity when the electricity price is high, and buy electricity when the electricity price is low, thereby earning benefits; and the parking lot earns benefits through the capacity electricity price. The so-called capacity electricity price is generally higher than the real-time electricity price, so that the parking lot can adjust the charging power of the electric automobile to reduce the capacity peak value on the premise of ensuring that the benefit of the electric automobile is not reduced, thereby making money. When there are a plurality of parking lots, competition between the parking lots and difference in electricity rates between the parking lots are also considered.
When a plurality of electric vehicles make a parking demand to the parking lot management platform, a time period for parking and an offer willing to be given in the time period for the parking demand, such as the number of dollars per hour, need to be given. When the management platform allocates the parking lot to the electric vehicle, it is necessary to consider competition between the parking lots and difference in electricity prices between the parking lots when there are a plurality of parking lots. In the process, the parking lot receives a plurality of requests for sharing the parking spaces sent by the parking lot management platform, the requests for parking the vehicles need to be calculated respectively, the income caused by the vehicles entering the parking lot for parking is calculated, and one electric vehicle with the vehicle parking request is selected to enter the parking lot according to the income. In the process, when the existing method is used for optimizing charging and discharging of the electric automobile, the nested objective function is difficult to solve by using a gradient correlation method, only typical random optimization algorithms including genetic algorithm, particle swarm algorithm, artificial bee colony algorithm and the like can be used, and the algorithms are quite unstable and consume less time and have low efficiency compared with the gradient correlation algorithm.
Disclosure of Invention
1. The technical problem to be solved is as follows:
in order to solve the technical problems, the invention provides a method and a system for calculating the matching benefit of a shared parking space and an electric vehicle in an electric power market.
2. The technical scheme is as follows:
the utility model provides a sharing parking stall and electric automobile make-up benefit calculation method under electric power market which characterized in that: the method comprises the following steps:
the method comprises the following steps: collecting parking lot information and vehicle information of a plurality of electric vehicles needing to calculate benefits; the parking lot information comprises the time for providing parking and the price of the parking space of the shared parking space provided by the parking lot; one of the electric vehicles needing the calculated benefit is selected to stop at the parking lot; the vehicle information of the electric vehicle needing the calculation benefit comprises a time period for parking the vehicle of the electric vehicle and a price for parking; wherein the electric vehicle requiring the calculated benefit is at least one.
Step two: carrying out price partition on the vehicle parking quotation of the used electric vehicle; the price partition is partitioned according to a preset price interval; the information of the electric vehicle in the highest price zone is selected to proceed with the following steps.
Step three: collecting electricity price information; the electricity price information is the electricity price information of the parking lot; the electricity price information is specifically the electricity price given by the power grid, and comprises time-of-use electricity price, peak electricity price and real-time electricity price.
Step four: performing power optimization on all the electric vehicles in the highest price range selected in the step two, and obtaining the lowest charging electricity fee according to the specific electricity price of the parking lot; the power optimization is to obtain all possible charge and discharge strategy sets of the electric automobile according to specific parameters of the automobile, wherein the specific parameters comprise the approach electric quantity, the highest electric quantity, the lowest electric quantity, the maximum charge power, the maximum discharge power, the approach time and the departure time; the power optimization is specifically optimized by adopting an improved Laplace multiplier method; the lowest charging electric charge is the electric charge corresponding to each strategy in the charging and discharging strategy set under the specific electricity price of the parking lot, and the lowest charging electric charge is the lowest value of the electric charges; and finally, outputting all possible charge and discharge power of each electric automobile from the approach to the departure every hour, namely a charge and discharge strategy set and the lowest electric charge.
Step five: deleting the charging and discharging strategies which are generated in the fourth step and are repeated in a centralized manner; and updating the charge and discharge strategy set of each electric automobile.
Step six: calculating the calculation estimated income of each electric vehicle remained in the fifth step according to the charge-discharge strategy set of each electric vehicle generated in the fourth step; the specifically calculating of the pre-estimated income comprises the following steps: and after calculating the average charging electric charge of the vehicle which is not subjected to power optimization, subtracting the minimum electric charge corresponding to the vehicle from the average charging electric charge of the vehicle.
Step seven: and performing a gradient optimization algorithm on the vehicle estimated income of all the electric vehicles generated in the step six, the charge and discharge strategy set of each electric vehicle generated in the step five and the load information of the parking lot so as to generate the income, namely the vehicle benefit, of each electric vehicle if the electric vehicle is parked in the parking lot.
Further, the improved Laplace multiplier method in the fourth step specifically comprises the following steps:
s41: objective function of electricity rate of ith vehicle:
Figure GDA0003169758440000021
(1) wherein f1 is the electric charge of the vehicle; pritThe electricity price of the power grid at the moment t; and Pit is the charging power of the ith trolley at the tth moment.
S42, the constraint condition of the formula (1) is as follows:
Figure GDA0003169758440000031
(2) in the formula, Pit is the charging power of the ith trolley at the tth moment; pmax is the maximum charging power; the time point is the entrance time of the ith trolley; tout.i is the departure time of the ith trolley; pmin is the minimum charging power, and the value is a negative number which represents that the electric automobile is discharging; emin is the lowest electrical quantity; ein.i is the electric quantity of the ith electric automobile when the ith electric automobile enters the field; emax is the maximum electric quantity of the electric automobile; sitThe state parameter of the ith trolley at the t moment is 1 when the vehicle is parked at the moment, otherwise, the state parameter is 0.
S43: an objective function that is substituted into the Laplace multiplier method, the objective function of the Laplace multiplier method being as follows:
Figure GDA0003169758440000032
(3) in the formula: a isi、bit、ciTAnd diTAny real number greater than or equal to zero; e.g. of the typeiAny real number other than zero.
S44: substituting an objective function of a Laplace multiplier method into a KKT condition equation set, wherein the KKT condition equation set is specifically as follows:
Figure GDA0003169758440000033
and S45, outputting a matrix formed by all solutions of the formula (4), namely the charge and discharge strategy set of the vehicle.
Further, the gradient algorithm specifically includes the following steps:
s71: generating a target function and a constraint condition of the total electric charge of the parking lot;
Figure GDA0003169758440000041
wherein f2 is the total parking electricity fee; part1 is parking lot power supplyElectric charge paid by the network; part2 is the electric charge paid by the electric automobile to the parking lot; part3 is the capacity electric charge paid to the power grid by the parking lot; pri is the power grid price; pp is the electricity price of the parking lot; pc is the capacity price; LObase is the base load of the parking lot; LOEVThe load of the electric automobile which enters the field is history; pmi is an optimal charging and discharging strategy of the electric vehicle i under the electricity price of the parking lot; pmir is all possible charge-discharge strategy sets of the electric automobile i; k is a constant, and a larger negative value is taken; pptThe electricity price of the parking lot at the t-th moment; pmirjA j charging strategy matrix is planted in a charging and discharging strategy set of an electric automobile i; row (Pm)irj) Is a policy matrix PmirjThe number of rows of (c).
The constraint conditions are as follows:
Figure GDA0003169758440000042
in the equation (6), the PEV is the predicted vehicle yield.
Constraint 1 ensures that the estimated income of the electric vehicle cannot be reduced, and constraint 2 ensures that the electricity price of the parking lot is positive and not more than the maximum value of the electricity price of the power grid.
S72: inputting the charge-discharge strategy matrix obtained in the step four, the basic load of the parking lot and the historical entrance vehicle load, and solving the target function formula (5) by using an interior point method; the solution is the electricity price given to the vehicle by the parking lot.
Further, the electricity rate at the time of the average charging of the vehicle is derived from the following equation:
Figure GDA0003169758440000043
in the formula (4) fAVE.iAverage charge rate for ith trolley; pAVE.iAnd charging power for average charging of the ith trolley.
A system for calculating the matching benefit of a shared parking space and an electric automobile in an electric power market comprises a vehicle information module to be evaluated, an improved Laplace multiplier method module, an automobile charging scheme module, a vehicle income pre-estimation module and a gradient optimization algorithm module.
The to-be-evaluated vehicle information module is used for acquiring parking lot information and vehicle information of a plurality of electric vehicles needing to calculate benefits; and outputs the information to the modified Laplace multiplier module.
The improved Laplace multiplier method module optimizes the power of the vehicle to be evaluated according to the information from the vehicle information module to be evaluated and the electricity price information of the parking lot, and outputs the optimized vehicle income to the vehicle income pre-estimating module; the power optimization method adopts an improved Laplace multiplier method.
The automobile charging scheme module is used for receiving all possible charging and discharging strategy sets of the vehicles input by the improved Laplace multiplier method module, deleting repeated vehicle charging and discharging strategies in the strategy sets, and inputting the charging and discharging strategy set information of all vehicles to be evaluated into the gradient optimization algorithm module.
The predicted vehicle income module is used for recording the optimal income of the vehicle to be estimated under the power grid price given by the power price module and inputting the predicted vehicle income information into the gradient optimization algorithm module.
The gradient optimization algorithm module is used for receiving vehicle estimated income information, vehicle charging and discharging strategy set information, parking lot load information and historical entrance vehicle information, obtaining vehicle benefit information of a parking lot and outputting the vehicle benefit information to the vehicle benefit module by constructing a parking lot income target function and applying a gradient optimization algorithm on the premise that the vehicle income is not lower than the estimated income, and meanwhile obtaining parking lot electricity price given by the parking lot when the parking lot carries out secondary optimization on the vehicle and outputting the parking lot electricity price information to the parking lot electricity price module.
3. Has the advantages that:
(1) according to the method, an improved Laplace multiplier method is adopted to obtain a charge-discharge strategy set of each electric vehicle with shared parking space requirements according to the electricity price information of the parking lot, and the lowest electricity charge and the charging scheme corresponding to the lowest electricity charge can be obtained through the charge-discharge strategy set and the corresponding electricity price value.
(2) The invention uses an improved Laplace multiplier method to convert the nested objective function into a differentiable equation set, and a gradient optimization algorithm is adopted to solve the objective equation.
(3) According to the method, the vehicle estimated income information is calculated firstly, then the parking lot income target function is constructed, the electricity cost of the parking lot is reduced while the vehicle income is not reduced, the benefit of the vehicle to the parking lot is calculated by using a gradient optimization algorithm, and the secondary optimization of charging and discharging of the electric vehicle in the parking lot is realized.
Drawings
FIG. 1 is a schematic diagram of a system for calculating matching efficiency between a shared parking space and an electric vehicle in an electric power market;
FIG. 2 is a diagram of an embodiment of an improved Rayleigh multiplier module in the present invention;
FIG. 3 is a flow chart of a solution of equation set (5) in step S44 in an exemplary embodiment;
FIG. 4 is a flow chart of a specific solution of the gradient optimization algorithm module in the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
As shown in the attached figure 1, the method for calculating the matching benefit of the shared parking space and the electric automobile in the electric power market is characterized by comprising the following steps of: the method comprises the following steps:
the method comprises the following steps: collecting parking lot information and vehicle information of a plurality of electric vehicles needing to calculate benefits; the parking lot information comprises the time for providing parking and the price of the parking space of the shared parking space provided by the parking lot; one of the electric vehicles needing the calculated benefit is selected to stop at the parking lot; the vehicle information of the electric vehicle needing the calculation benefit comprises a time period for parking the vehicle of the electric vehicle and a price for parking; wherein the electric vehicle requiring the calculated benefit is at least one.
Step two: carrying out price partition on the vehicle parking quotation of the used electric vehicle; the price partition is partitioned according to a preset price interval; the information of the electric vehicle in the highest price zone is selected to proceed with the following steps.
Step three: collecting electricity price information; the electricity price information is the electricity price information of the parking lot; the electricity price information is specifically the electricity price given by the power grid, and comprises time-of-use electricity price, peak electricity price and real-time electricity price.
Step four: performing power optimization on all the electric vehicles in the highest price range selected in the step two, and obtaining the lowest charging electricity fee according to the specific electricity price of the parking lot; the power optimization is to obtain all possible charge and discharge strategy sets of the electric automobile according to specific parameters of the automobile, wherein the specific parameters comprise the approach electric quantity, the highest electric quantity, the lowest electric quantity, the maximum charge power, the maximum discharge power, the approach time and the departure time; the power optimization is specifically optimized by adopting an improved Laplace multiplier method; the lowest charging electric charge is the electric charge corresponding to each strategy in the charging and discharging strategy set under the specific electricity price of the parking lot, and the lowest charging electric charge is the lowest value of the electric charges; and finally, outputting all possible charge and discharge power of each electric automobile from the approach to the departure every hour, namely a charge and discharge strategy set and the lowest electric charge.
Step five: deleting the charging and discharging strategies which are generated in the fourth step and are repeated in a centralized manner; and updating the charge and discharge strategy set of each electric automobile.
Step six: calculating the calculation estimated income of each electric vehicle remained in the fifth step according to the charge-discharge strategy set of each electric vehicle generated in the fourth step; the specifically calculating of the pre-estimated income comprises the following steps: and after calculating the average charging electric charge of the vehicle which is not subjected to power optimization, subtracting the minimum electric charge corresponding to the vehicle from the average charging electric charge of the vehicle.
Step seven: and performing a gradient optimization algorithm on the vehicle estimated income of all the electric vehicles generated in the step six, the charge and discharge strategy set of each electric vehicle generated in the step five and the load information of the parking lot so as to generate the income, namely the vehicle benefit, of each electric vehicle if the electric vehicle is parked in the parking lot.
Further, as shown in fig. 2, the modified lagrange multiplier method in step four specifically includes the following steps:
s41: objective function of electricity rate of ith vehicle:
Figure GDA0003169758440000071
(1) wherein f1 is the electric charge of the vehicle; pritThe electricity price of the power grid at the moment t; and Pit is the charging power of the ith trolley at the tth moment.
S42, the constraint condition of the formula (1) is as follows:
Figure GDA0003169758440000072
(2) in the formula, Pit is the charging power of the ith trolley at the tth moment; pmax is the maximum charging power; the time point is the entrance time of the ith trolley; tout.i is the departure time of the ith trolley; pmin is the minimum charging power, and the value is a negative number which represents that the electric automobile is discharging; emin is the lowest electrical quantity; ein.i is the electric quantity of the ith electric automobile when the ith electric automobile enters the field; emax is the maximum electric quantity of the electric automobile; sitThe state parameter of the ith trolley at the t moment is 1 when the vehicle is parked at the moment, otherwise, the state parameter is 0.
S43: an objective function that is substituted into the Laplace multiplier method, the objective function of the Laplace multiplier method being as follows:
Figure GDA0003169758440000073
(3) in the formula: a isi、bit、ciTAnd diTAny real number greater than or equal to zero; e.g. of the typeiAny real number other than zero.
S44: substituting an objective function of a Laplace multiplier method into a KKT condition equation set, wherein the KKT condition equation set is specifically as follows:
Figure GDA0003169758440000074
and S45, outputting a matrix formed by all solutions of the formula (4), namely the charge and discharge strategy set of the vehicle.
Further, as shown in fig. 4, the gradient algorithm specifically includes the following steps:
s71: generating a target function and a constraint condition of the total electric charge of the parking lot;
Figure GDA0003169758440000081
wherein f2 is the total parking electricity fee; part1 is the electricity degree electric charge paid to the power grid by the parking lot; part2 is the electric charge paid by the electric automobile to the parking lot; part3 is the capacity electric charge paid to the power grid by the parking lot; pri is the power grid price; pp is the electricity price of the parking lot; pc is the capacity price; LObase is the base load of the parking lot; LOEVThe load of the electric automobile which enters the field is history; pmi is an optimal charging and discharging strategy of the electric vehicle i under the electricity price of the parking lot; pmir is all possible charge-discharge strategy sets of the electric automobile i; k is a constant, and a larger negative value is taken; pptThe electricity price of the parking lot at the t-th moment; pmirjA j charging strategy matrix is planted in a charging and discharging strategy set of an electric automobile i; row (Pm)irj) Is a policy matrix PmirjThe number of rows of (c).
The constraint conditions are as follows:
Figure GDA0003169758440000082
in (6), the PEV is the predicted vehicle yield.
Constraint 1 ensures that the estimated income of the electric vehicle cannot be reduced, and constraint 2 ensures that the electricity price of the parking lot is positive and not more than the maximum value of the electricity price of the power grid.
S72: inputting the charge-discharge strategy matrix obtained in the step four, the basic load of the parking lot and the historical entrance vehicle load, and solving the target function formula (5) by using an interior point method; the solution is the electricity price given to the vehicle by the parking lot.
Further, the electricity rate at the time of the average charging of the vehicle is derived from the following equation:
Figure GDA0003169758440000091
in the formula (7) fAVE.iAverage charge rate for ith trolley; pAVE.iAnd charging power for average charging of the ith trolley.
A system for calculating the matching benefit of a shared parking space and an electric automobile in an electric power market comprises a vehicle information module to be evaluated, an improved Laplace multiplier method module, an automobile charging scheme module, a vehicle income pre-estimation module and a gradient optimization algorithm module.
The to-be-evaluated vehicle information module is used for acquiring parking lot information and vehicle information of a plurality of electric vehicles needing to calculate benefits; and outputs the information to the modified Laplace multiplier module.
The improved Laplace multiplier method module optimizes the power of the vehicle to be evaluated according to the information from the vehicle information module to be evaluated and the electricity price information of the parking lot, and outputs the optimized vehicle income to the vehicle income pre-estimating module; the power optimization method adopts an improved Laplace multiplier method.
The automobile charging scheme module is used for receiving all possible charging and discharging strategy sets of the vehicles input by the improved Laplace multiplier method module, deleting repeated vehicle charging and discharging strategies in the strategy sets, and inputting the charging and discharging strategy set information of all vehicles to be evaluated into the gradient optimization algorithm module.
The predicted vehicle income module is used for recording the optimal income of the vehicle to be estimated under the power grid price given by the power price module and inputting the predicted vehicle income information into the gradient optimization algorithm module.
The gradient optimization algorithm module is used for receiving vehicle estimated income information, vehicle charging and discharging strategy set information, parking lot load information and historical entrance vehicle information, obtaining vehicle benefit information of a parking lot and outputting the vehicle benefit information to the vehicle benefit module by constructing a parking lot income target function and applying a gradient optimization algorithm on the premise that the vehicle income is not lower than the estimated income, and meanwhile obtaining parking lot electricity price given by the parking lot when the parking lot carries out secondary optimization on the vehicle and outputting the parking lot electricity price information to the parking lot electricity price module.
FIG. 3 is a flowchart of the solution of equation set (5) in step S44; the solving process specifically comprises the following steps:
step S441: constructing a Laplace multiplier method expression of a target function and a constraint condition and converting the Laplace multiplier method expression into a KKT condition equation set;
the expression of the target function and the constraint condition by the Laplace multiplier is input by the Laplace multiplier module 2322, and the KKT condition equation set is input by the KKT condition module 2324.
Step S442: for a KKT conditional equation set of x unknowns, 4x inequality constraints and one equality constraint;
step S443: selecting (x-1) inequality constraints from 4x inequality constraints, and forming a sub equation set with the equality constraints;
step S444: all the sub-equation sets form a sub-equation set;
step S445: deleting the sub equation sets with the inequality constraints conflicting with each other from the equation set;
step S446: each set of sub-equations is solved and all solutions form a solution set.
It should be noted that the unknown quantity of the equation is set as the charge-discharge power of the vehicle per hour, so the solution of each sub-equation set is the charge-discharge strategy of the vehicle, and the solution set of the equation set is all possible charge-discharge strategy sets of the vehicle.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. The utility model provides a sharing parking stall and electric automobile make-up benefit calculation method under electric power market which characterized in that: the method comprises the following steps:
the method comprises the following steps: collecting parking lot information and vehicle information of a plurality of electric vehicles needing to calculate benefits; the parking lot information comprises the time for providing parking and the price of the parking space of the shared parking space provided by the parking lot; one of the electric vehicles needing the calculated benefit is selected to stop at the parking lot; the vehicle information of the electric vehicle needing the calculation benefit comprises a time period for parking the vehicle of the electric vehicle and a price for parking; wherein the electric vehicle needing the calculated benefit is at least one;
step two: carrying out price partition on the vehicle parking quotation of the used electric vehicle; the price partition is partitioned according to a preset price interval; the information of the electric vehicle in the highest price zone is selected to proceed to the following steps,
step three: collecting electricity price information; the electricity price information is the electricity price information of the parking lot; the electricity price information is specifically the electricity price given by the power grid, and comprises time-of-use electricity price, peak electricity price and real-time electricity price;
step four: performing power optimization on all the electric vehicles in the highest price range selected in the step two, and obtaining the lowest charging electricity fee according to the specific electricity price of the parking lot; the power optimization is to obtain all possible charge and discharge strategy sets of the electric automobile according to specific parameters of the automobile, wherein the specific parameters comprise the approach electric quantity, the highest electric quantity, the lowest electric quantity, the maximum charge power, the maximum discharge power, the approach time and the departure time; the power optimization is specifically optimized by adopting an improved Laplace multiplier method; the lowest charging electric charge is the electric charge corresponding to each strategy in the charging and discharging strategy set under the specific electricity price of the parking lot, and the lowest charging electric charge is the lowest value of the electric charges; finally, outputting all possible charge and discharge power of each electric automobile from the approach to the departure every hour, namely a charge and discharge strategy set and the lowest electric charge;
step five: deleting the charging and discharging strategies which are generated in the fourth step and are repeated in a centralized manner; updating a charge-discharge strategy set of each electric automobile;
step six: calculating the calculation estimated income of each electric vehicle remained in the fifth step according to the charge-discharge strategy set of each electric vehicle generated in the fourth step; the specifically calculating of the pre-estimated income comprises the following steps: after calculating the average charging electric charge of the vehicle which is not optimized by power, subtracting the lowest electric charge corresponding to the vehicle from the average charging electric charge of the vehicle;
step seven: performing gradient optimization algorithm on the vehicle estimated income of all the electric vehicles generated in the step six, the charge and discharge strategy set of each electric vehicle generated in the step five and the load information of the parking lot so as to generate the income, namely the vehicle benefit, of each electric vehicle if the electric vehicle is parked in the parking lot;
the improved Laplace multiplier method in the fourth step specifically comprises the following steps:
s41: objective function of electricity rate of ith vehicle:
Figure FDA0003186424410000011
(1) wherein f1 is the electric charge of the vehicle; pritThe electricity price of the power grid at the moment t; pit is the charging power of the ith trolley at the tth moment;
s42, the constraint condition of the formula (1) is as follows:
Figure FDA0003186424410000021
(2) in the formula, Pit is the charging power of the ith trolley at the tth moment; pmax is the maximum charging power; the time point is the entrance time of the ith trolley; tout.i is the departure time of the ith trolley; pmin is the minimum charging power, and the value is a negative number which represents that the electric automobile is discharging; emin is the lowest electrical quantity; ein.i is the electric quantity of the ith electric automobile when the ith electric automobile enters the field; emax is the maximum electric quantity of the electric automobile; sitThe state parameter of the ith trolley at the tth moment is 1 when the trolley parks at the moment, otherwise, the state parameter is 0;
s43: an objective function that is substituted into the Laplace multiplier method, the objective function of the Laplace multiplier method being as follows:
Figure FDA0003186424410000022
(3) in the formula: a isi、bit、ciTAnd diTAny real number greater than or equal to zero; e.g. of the typeiIs any real number other than zero;
s44: substituting an objective function of a Laplace multiplier method into a KKT condition equation set, wherein the KKT condition equation set is specifically as follows:
Figure FDA0003186424410000023
and S45, outputting a matrix formed by all solutions of the formula (4), namely the charge and discharge strategy set of the vehicle.
2. The method for calculating the matching benefit of the shared parking space and the electric automobile in the electric power market according to claim 1, characterized by comprising the following steps of: the gradient optimization algorithm specifically comprises the following steps:
s71: generating a target function and a constraint condition of the total electric charge of the parking lot;
Figure FDA0003186424410000031
wherein f2 is the total parking electricity fee; part1 is the electricity degree electric charge paid to the power grid by the parking lot; part2 is the electric charge paid by the electric automobile to the parking lot; part3 is the capacity electric charge paid to the power grid by the parking lot; pri is the power grid price; pp is the electricity price of the parking lot; pc is the capacity price; LObaseA base load for a parking lot; LOEVThe load of the electric automobile which enters the field is history; pmi is an optimal charging and discharging strategy of the electric vehicle i under the electricity price of the parking lot; pmir is all possible charge-discharge strategy sets of the electric automobile i; k is a constant; pptThe electricity price of the parking lot at the t-th moment; pmirjA j charging strategy matrix is planted in a charging and discharging strategy set of an electric automobile i; row (Pm)irj) Is a policy matrix PmirjThe number of rows of (c);
the constraint conditions are as follows:
Figure FDA0003186424410000032
in the formula (6), PEV is the predicted vehicle income;
constraint 1 ensures that the estimated income of the electric vehicle cannot be reduced, and constraint 2 ensures that the electricity price of the parking lot is positive and not more than the maximum value of the electricity price of the power grid;
s72: inputting the charge-discharge strategy matrix obtained in the fourth step, the basic load of the parking lot and the historical entrance vehicle load, and solving the target function formula (5) by using an interior point method, a projection method, a Newton method or a conjugate gradient method; the solution is the electricity price given to the vehicle by the parking lot.
3. The method for calculating the matching benefit of the shared parking space and the electric automobile in the electric power market according to claim 1, characterized by comprising the following steps of: the electricity rate at the time of average charging of the vehicle is given by:
Figure FDA0003186424410000041
in the formula (7) fAVE.iAverage charge rate for ith trolley; pAVE.iAnd charging power for average charging of the ith trolley.
4. A system for calculating the matching benefit of a shared parking space and an electric vehicle in an electric power market by applying the method for calculating the matching benefit of the shared parking space and the electric vehicle in the electric power market according to any one of claims 1 to 3, is characterized in that: the system comprises a vehicle information module to be evaluated, an improved Laplace multiplier method module, an automobile charging scheme module, a vehicle income pre-estimation module and a gradient optimization algorithm module;
the to-be-evaluated vehicle information module is used for acquiring parking lot information and vehicle information of a plurality of electric vehicles needing to calculate benefits; and outputting the information to an improved Laplace multiplier method module;
the improved Laplace multiplier method module optimizes the power of the vehicle to be evaluated according to the information from the vehicle information module to be evaluated and the electricity price information of the parking lot, and outputs the optimized vehicle income to the vehicle income pre-estimating module; the power optimization method adopts an improved Laplace multiplier method;
the automobile charging scheme module is used for receiving all possible charging and discharging strategy sets of the vehicles input by the improved Laplace multiplier method module, deleting repeated vehicle charging and discharging strategies in the strategy sets, and inputting the charging and discharging strategy set information of all vehicles to be evaluated into the gradient optimization algorithm module;
the estimated vehicle income module is used for recording the optimal income of the vehicle to be estimated under the power grid price given by the power price module and inputting the estimated vehicle income information into the gradient optimization algorithm module;
the gradient optimization algorithm module is used for receiving vehicle estimated income information, vehicle charging and discharging strategy set information, parking lot load information and historical entrance vehicle information, obtaining vehicle benefit information of a parking lot and outputting the vehicle benefit information to the vehicle benefit module by constructing a parking lot income target function and applying a gradient optimization algorithm on the premise that the vehicle income is not lower than the estimated income, and meanwhile obtaining parking lot electricity price given by the parking lot when the parking lot carries out secondary optimization on the vehicle and outputting the parking lot electricity price information to the parking lot electricity price module.
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