CN115848196A - Electric automobile ordered charging guide method based on dynamic demand and new energy consumption - Google Patents
Electric automobile ordered charging guide method based on dynamic demand and new energy consumption Download PDFInfo
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Abstract
The invention discloses an electric automobile ordered charging guiding method based on dynamic requirements and new energy consumption, which comprises the following steps: the electric automobile user directly obtains the battery reserve information, predicts the next charging period at the same time, directly provides the position information and the utilization rate information of the charging station in the target area along the way and near the parking and the position information of all the electric automobiles waiting to be charged for the user, and knows the charge of one-time charging, thereby optimizing the load of the power grid while ensuring the sufficient electric quantity of the user. The invention provides a dynamic time-of-use electricity price strategy considering the output condition of new energy, which guides the electric automobile to be charged orderly so as to realize the on-site consumption of the new energy, takes the minimum total charging cost of a user, the optimum electric quantity reserve during traveling and the minimum peak-valley difference of the load of a power grid as optimization targets, comprehensively considers the constraint conditions of the charging requirement of the user, the output of the new energy and the like, and establishes a multi-objective optimization model which is based on the dynamic time-of-use electricity price and meets the dynamic charging requirement.
Description
Technical Field
The invention relates to an electric automobile ordered charging guide system, in particular to an electric automobile ordered charging guide method based on dynamic requirements and new energy consumption, and belongs to the technical field of new energy.
Background
With the continuous increase of the reserved quantity of electric automobiles, the load of a power grid is increased by randomly charging a large number of electric automobiles, so that the overload phenomenon of the power grid in a part of regions is caused, and the stable operation of the power grid is influenced. However, the renewable energy has large total generated energy, intermittent output and difficult grid connection and consumption, and the long-term development of the renewable energy industry in China is seriously influenced. Under the condition, the new energy automobile is guided to be charged orderly by combining the dynamic trip condition of the new energy automobile and new energy consumption research, the service life of a battery of the new energy automobile is guaranteed not to be influenced, the power grid load fluctuation can be effectively restrained while the trip electric quantity demand of a user is guaranteed, and the method is suitable for large-scale development of new energy.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides an electric vehicle ordered charging guiding method based on dynamic requirements and new energy consumption, and the problem of unstable power grid operation caused by random charging of the existing electric vehicle is solved by providing a new energy electric vehicle charging guiding scheme.
In order to solve the technical problems, the invention adopts the technical scheme that: an electric vehicle ordered charging guide method based on dynamic requirements and new energy consumption, the method comprises the following steps: the method comprises the steps that an electric automobile user directly obtains battery reserve information, the next charging period is predicted at the same time, position information and utilization rate information of a charging station in a target area along the way and near the parking and position information of all electric automobiles waiting to be charged are directly provided for the user, the user knows the charge for one-time charging, and the power grid load is optimized while the electric quantity of the user is sufficient.
The method specifically comprises the following steps:
s1, acquiring position information, cruising information and related information of all electric vehicles which send charging demands in a target area, and power grid load conditions of the area;
s2, obtaining a plurality of guiding scheme information according to given limiting conditions, position information, cruising information and road condition information of all electric vehicles to be charged and related information of all operable charging piles;
s3, calculating power consumption conditions of a plurality of charging pile charging schemes according to preset charging power grid load conditions;
and S4, sequencing the electric network charges of the plurality of guidance scheme information in an ascending order, and displaying the guidance scheme information according to a sequencing result.
Preferably, in step S1, the acquired information further includes:
the electric vehicle charging system comprises electric quantity information and position information of the electric vehicle which is in urgent need of charging and position information of the electric vehicle under the condition that the travel power is met but the electric quantity reserve is not enough.
Preferably, in step S2, the given limiting conditions include:
when the electric automobile to be charged sends charging information, the cruising distance can reach the fixed charging pile, and the idle charging pile can be used for charging, the system can provide charging route guidance for the user and give optimal charging time at the same time by taking minimum cost as a target, the time period only needs to meet the trip mileage of the user without being fully charged, and meanwhile, the battery utilization rate of the electric automobile is well protected;
the method further comprises the steps of guiding the charging scheme to be charged for a given time which is not longer than the time when the battery is fully charged, and guiding the charging pile to be positioned in an area with lower load of a power grid.
Preferably, in the step S4, the grid charges of the plurality of guidance plan information are sorted in an ascending order, including sorting the charging time and the position guidance of the charging pile in an ascending order with the shortest waiting time;
if the charging time of at least two guidance schemes is the same when sequencing display is carried out according to the charging time of all the power grid charges, sequencing the utilization rates of the charging piles of at least two guidance schemes in a descending order;
and if the waiting time of the charging guidance according to the plurality of guidance scheme information is the same and the utilization rate of the charging piles is also the same, performing ascending sequencing on the grid loads under the current grid utilization.
Preferably, the method uses a system comprising:
the data acquisition module is used for acquiring the position information and the cruising information of all electric vehicles in a target area, the information of chargeable or properly-charged electric vehicles, the position information of the working charging piles and the power utilization information of a power grid in the area;
the algorithm module is used for predicting whether new car waiting information exists or not according to given limiting conditions, position information and cruising information of all the electric cars to be charged;
the algorithm module is also used for calculating the power grid load quantities of the plurality of pieces of guide charging scheme information according to a preset response time rule and sequencing the power grid load quantities of the plurality of pieces of guide charging scheme information in an ascending order;
and the display output module is used for displaying the preset scheme information from small to large of the load capacity of the power grid.
Preferably, the algorithm module:
the given limiting conditions comprise that the electric automobile which needs to be charged urgently is not required to be properly charged near the working charging pile for supplementing the electric quantity, the system gives charging guidance and gives the scheme of saving the most money, if the electric automobile is charged at the moment, the price is low, the load of a power grid can be relieved, and the utilization rate of the power grid is optimized to achieve the energy consumption absorption effect;
sorting the charging fees of the electric vehicles in an ascending order according to the charging scheme information; and if the charging cost is the same, sequencing according to the ascending order of the power grid load in the area after the charging pile is used.
Preferably, the algorithm module sets a constraint condition according to the genetic algorithm, the constraint condition being:
a. and power balance constraint: p n (t)+P grid (t)＝P b (t)+P ev (t)+P loss (t)
Wherein, P n (t) is the power generated by the energy source in the time period t, P grid (t) purchasing electric quantity from the power grid in a period t; p b (t) power distribution network base load, P, at time t loss (t) line network loss for a period of t;
b. and (3) constraint of charging time: the charging time for guiding the electric automobile is less than or equal to the time required by fully charging the battery;
c. constraint of charging demand: and when the electric automobile is charged, the power grid state of charge value is not larger than the power grid state of charge value of the electric automobile after the electric automobile user is fully charged.
The genetic algorithm based on the evolutionary theory can effectively find an approximate global optimal solution. In order to meet the charging time sequence constraint, the power load constraint of a power grid during charging and the quantity constraint of charging vehicles needing to be charged, the genetic algorithm is improved. The genetic algorithm is to calculate charging piles which can be used for charging, and then calculate the optimal path.
Preferably, the genetic algorithm comprises the steps of:
respectively generating initial populations meeting a forward construction time sequence and a reverse construction time sequence based on the construction layout of the previous/next stage and the distribution condition of the expressway service areas;
step 2, calculating population fitness;
judging whether the population meets service level constraint and network accessibility constraint as indexes for representing the individual quality degree, if so, selecting a fitness function as the reciprocal of a target function, otherwise, selecting the reciprocal of an infinite penalty value M;
step 3, selection process: selecting a population by adopting a roulette algorithm;
step 4, a crossing process: single-point crossing is adopted for chromosome crossing, so that the feasibility of a solution is ensured;
step 5, mutation process: aiming at a forward construction time sequence and a reverse construction time sequence, single-point variation rules meeting construction time sequence constraints are respectively adopted.
The chromosome is a two-dimensional matrix and comprises 2 decision variables, wherein firstly, the charging station can provide charging; a dummy variable, 1 indicates that charging service can be provided, otherwise 0; secondly, the number of the charging piles which can work is p, if the charging piles can work, the number of the charging piles which can work in the site is p min ,p max ]Inner selection, the algorithm is shown in fig. 1.
Preferably, the calculation process of the optimal ordered charging path guidance comprises the following steps: as shown in fig. 2, the charging pile which can work is calculated and then the electric vehicle is guided to be charged in the past.
S1, constructing a multi-user effective path set, and circulating: the travel time T belongs to T, and T is T time periods divided by equal time intervals in one day;
s2, updating travel cost and selecting charging probability according to the charging facility state at the last time t-1, dynamically deducing traffic flow, and setting iteration number n =1 for newly increased flow distribution, flow transmission and queuing charging in sequence;
s3, recalculating generalized trip expenses and selection probabilities of all paths of the fuel vehicle and the electric vehicle according to the flow distribution after the last iteration, the charging facility working condition in the network, the road section load and the vehicle state; (recalculation is based on traffic flow prediction)
S4, carrying out dynamic deduction on the traffic flow to finally obtain an additional flow distribution result; (the distribution result is used to calculate the optimal charging guide route)
And S5, calculating the road network flow after the iteration by adopting an iteration weighting method. (calculate the best charging website, guide the charging route)
The optimal ordered charging path guidance process is calculated through the steps 1 to 5, because the optimal path guidance can be calculated only by combining the distribution of the road network and the current power grid load condition, because not only the power grid load but also the electric quantity of the electric vehicle can be supported to the optimal charging place, and the rechargeable electric vehicle which can be recharged can be guided to the optimal charging place.
The invention provides a dynamic time-of-use electricity price strategy considering the output condition of new energy, which guides the electric automobile to be charged orderly so as to realize the local consumption of the new energy, takes the minimum total charging cost of a user, the optimum electric quantity reserve during traveling and the minimum peak-valley difference of the load of a power grid as optimization targets, comprehensively considers the constraint conditions of the charging requirements (the charging requirements during idle time and the sudden charging requirements) of the user, the output of the new energy and the like, and establishes a multi-objective optimization model which is based on the dynamic time-of-use electricity price and meets the dynamic charging requirements.
Drawings
FIG. 1 is a flow chart of the genetic algorithm of the present invention.
FIG. 2 is a flowchart of the calculation process for optimal ordered charging path guidance of the present invention.
FIG. 3 is a load graph after ordered guided charging in an embodiment of the present invention.
Fig. 4 is a power grid charge optimization diagram after ordered guidance charging in the embodiment of the present invention.
Fig. 5 is a diagram of an electric vehicle charging guidance system according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
Example 1
The method comprises the steps of firstly analyzing data information required to be charged when the electric automobile goes out and position information of an existing fixed charging pile to obtain a network point where a mobile electricity supplementing car needs to be arranged.
The starting charge time x satisfies the normal distribution, probability density function:
in formula (1): mu.s s ＝17.6；σ s ＝3.4。
The daily driving distance meets the logarithmic normal distribution, and the probability density is shown as the formula (2):
in formula (2): sigma D ＝3.2；μ D ＝0.88。
According to the charging characteristics of the power battery of the electric automobile, in order to simplify calculation, the charging process of the electric automobile can be regarded as constant-power charging:
P＝P c (3)
the electric vehicle has the following charge state:
SOC＝(1-d/d m )SOC' (4)
wherein d is the daily driving distance of the new energy electric automobile, d m The maximum driving distance of the new energy electric automobile is obtained; SOC' is the state of charge before the first trip.
The charging time of a single electric automobile is as follows:
in the formula, t c Is the charging time; and C is the battery capacity of the electric automobile.
The electric automobile cluster load is obtained by accumulating the charging loads of all electric automobiles in the area:
in the formula, P ev (t) charging load of the electric vehicle in a period of t; n is the number of the electric automobiles; p ev,i And (t) is the charging power of the electric automobile i in the t period.
The system design is a control mode using electricity price excitation, and provides a time-of-use electricity price strategy formulated according to the output of new energy and the dynamic charging demand of the electric automobile during traveling, so as to promote the consumption of the new energy.
And calculating the charging price of each time period according to the predicted value of the new energy output in each time period in one day, dividing the day into T time periods according to the equal time interval delta T, and dividing the charging price of the T time periods into 3 stages of high-low-medium according to the size of the predicted value of the new energy output in each time period. 125% of the new energy output exceeding the average value in the T period is the high output period and the low electricity price; the average value of the time periods lower than T is 75% of the low-output time period and the high electricity price; between the two is flat load time interval electricity price. The high and low electricity prices float 60% on the basis of the electricity prices in the ordinary period respectively.
The relation between the charging price and the predicted power generated by the new energy is as follows:
s (t) represents the charge price at the t-th time of day; s 0 Fixed electricity price for random charging of disorder, P n ' (t) is predicted power of new energy power generation in a period t,
and predicting the average power value for the new energy power generation in one day, wherein T is the number of electroplating period time segments in one day.
The charging price of the electric automobile changes along with the generated energy of the new energy, and the charging price s (t) is lower in the stage of high output of the new energy; on the contrary, the charging price s (t) is higher at the stage of low new energy output. Under the prerequisite that does not influence the normal trip of user, the electric automobile user can select as far as possible to charge at the price of electricity low ebb section for reducing the expense of charging, can consider the selection of filling electric pile during the trip simultaneously, selects as far as possible in the lower region of electric load, avoids certain regional electric charge to bear overweight. This strategy may focus the charging load to a period of time when the new energy is being exported.
Constraint conditions are as follows:
(1) And power balance constraint: p n (t)+P grid (t)＝P b (t)+P ev (t)+P loss (t)
P n (t) is the power generated by the energy source in the period of t, P grid (t) purchasing electric quantity from the power grid in a period t; p b (t) power distribution network base load, P, at time t loss (t) is the line network loss over a period of t.
(2) And (3) constraint of charging time: the charging time for guiding the new energy automobile is less than or equal to the time required by fully charging the battery.
(3) Constraint of charging demand: and when the electric vehicle is charged, the state of charge value of the power grid is not greater than the state of charge value of the power grid after the electric vehicle user is fully charged.
And (3) performing optimal scheduling solution on the model by adopting a genetic algorithm, wherein the model is shown in figure 1. There are many constraints in genetic algorithm code computation, among which are constraints.
And step 2, calculating population fitness. And judging whether the population meets the service level constraint and the network accessibility constraint as indexes for representing the individual quality degree, if so, selecting the fitness function as the reciprocal of the target function, and otherwise, selecting the fitness function as the reciprocal of an infinite penalty value M.
And 3, selecting a process. And selecting the population by adopting a roulette algorithm.
Step 4, the process is crossed. And single-point crossing is adopted for chromosome crossing, so that the feasibility of the solution is ensured.
And 5, performing mutation process. Aiming at the forward construction time sequence and the reverse construction time sequence, a single-point variation rule meeting the construction time sequence constraint is respectively adopted.
And selecting the measured data of a charging station in a certain area to perform electric vehicle charging simulation, wherein the capacity of the charging station provided with a wind generating set is 700kW, and the scale of service vehicles is 100. The actual grid data and the grid load factor after the electric vehicle is guided are shown in fig. 3 and fig. 4. Fig. 3 is a graph of the ordered lead post-charge load. Fig. 4 is a grid charge optimization diagram after ordered guide charging. Fig. 5 is a diagram of an electric vehicle charging guidance system.
As can be seen from fig. 3 and 4, the optimal charging guidance scheme is calculated according to the genetic algorithm by the method and the system for orderly charging the new energy electric vehicle, so that the grid pressure can be effectively relieved. The system can guide the new energy automobile to charge at any time and any place, the charging time is long, and the charging pile and the mobile charging device which can be used for charging are reconfigured, so that the charging requirement is met, the load valley difference of a power grid can be reduced, and the total charging cost of a new energy automobile user is the lowest.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.
Claims (10)
1. Electric automobile ordered charging guide method based on dynamic demand and new energy consumption is characterized in that: the method comprises the following steps: the electric automobile user directly obtains the battery reserve information, predicts the next charging period at the same time, directly provides the position information and the utilization rate information of the charging station in the target area along the way and near the parking and the position information of all the electric automobiles waiting to be charged for the user, and knows the charge of one-time charging, thereby optimizing the load of the power grid while ensuring the sufficient electric quantity of the user.
2. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 1, characterized in that: the method specifically comprises the following steps:
s1, acquiring position information, cruising information and related information of all electric vehicles which send charging demands in a target area, and power grid load conditions of the area;
s2, obtaining a plurality of guiding scheme information according to given limiting conditions, position information, cruising information and road condition information of all electric vehicles to be charged and related information of all operable charging piles;
s3, calculating the power consumption of the power grid when the regional charging pile works according to the preset charging power grid load condition by taking the multi-region charging pile work as an assumed condition;
and S4, sequencing the electric network charges of the plurality of guidance scheme information in an ascending order, and displaying the guidance scheme information according to the sequencing result.
3. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 2, characterized in that: in step S1, the acquired information further includes:
the electric vehicle charging system comprises electric quantity information and position information of the electric vehicle which is in urgent need of charging and position information of the electric vehicle under the condition that the travel power is met but the electric quantity reserve is not enough.
4. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 2, characterized in that: in step S2, the given limiting conditions include:
when the electric automobile to be charged sends charging information, the cruising distance can reach the fixed charging pile, and the idle charging pile can be used for charging, the system can provide charging route guidance for the user and give optimal charging time at the same time by taking minimum cost as a target, the time period only needs to meet the trip mileage of the user without being fully charged, and meanwhile, the battery utilization rate of the electric automobile is well protected;
the method further comprises the step of guiding the charging scheme to be charged for a given time which is not more than the time when the battery is fully charged, and the position of the charging pile is in an area with low load of the power grid.
5. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 2, characterized in that: in the step S4, the power grid charges of the plurality of guidance plan information are sorted in an ascending order, including the ascending order of the charging time and the position guidance of the charging pile with the shortest waiting time;
if the charging time of at least two guidance schemes is the same when sequencing display is carried out according to the charging time of all the power grid charges, the utilization rates of the charging piles of at least two guidance schemes are sequenced in a descending order;
and if the waiting time of the charging guidance according to the plurality of guidance scheme information is the same and the utilization rate of the charging piles is also the same, performing ascending sequencing on the grid loads under the current grid utilization.
6. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 1, characterized in that: the method uses a system comprising:
the data acquisition module is used for acquiring the position information and the cruising information of all electric vehicles in a target area, the information of chargeable or properly-charged electric vehicles, the position information of the working charging piles and the power utilization information of a power grid in the area;
the algorithm module is used for predicting whether new vehicle waiting information exists or not according to given limiting conditions, position information and cruising information of all the electric vehicles to be charged;
the algorithm module is also used for calculating the power grid load quantities of the plurality of pieces of guide charging scheme information according to a preset response time rule and sequencing the power grid load quantities of the plurality of pieces of guide charging scheme information in an ascending order;
and the display output module is used for displaying the preset scheme information from small to large of the load capacity of the power grid.
7. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 6, characterized in that: in the algorithm module:
the given limiting conditions comprise that the electric automobile which needs to be charged urgently is not required to be properly charged near the operable charging pile for replenishing electric quantity, the system gives charging guidance and gives the most money-saving scheme, if the electric automobile is charged at the moment, the price is low, the load of a power grid can be relieved, and the utilization rate of the power grid is optimized to achieve the energy consumption absorption effect;
sorting according to the ascending order of the charging cost of the electric automobile in the plurality of charging scheme information; and if the charging cost is the same, sequencing according to the ascending order of the power grid load in the area after the charging pile is used.
8. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 6, characterized in that: the algorithm module sets constraint conditions according to the genetic algorithm, wherein the constraint conditions are as follows:
a. and power balance constraint: p n (t)+P grid (t)＝P b (t)+P ev (t)+P loss (t)
Wherein, P n (t) is the power generated by the energy source in the period of t, P grid (t) purchasing electric quantity from the power grid in a period t; p b (t) power distribution network base load, P, at time t loss (t) line network loss for a period of t;
b. and (3) constraint of charging time: the charging time for guiding the electric automobile is less than or equal to the time required by fully charging the battery;
c. constraint of charging demand: and when the electric automobile is charged, the power grid state of charge value is not greater than the power grid state of charge value of the electric automobile user after the electric automobile user is fully charged.
9. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 8, characterized in that: the genetic algorithm comprises the following steps:
step 1, generating an initial population;
respectively generating initial populations meeting a forward construction time sequence and a reverse construction time sequence based on the power grid load distribution condition caused by the charging pile in the previous/next stage when working in the area; (this is an applied innovation of genetic algorithm)
Step 2, calculating population fitness;
judging whether the population meets charging time sequence constraints, power grid load constraints during charging and charging pile quantity constraints capable of being charged, and taking the population as an index for representing the individual quality degree;
step 3, selection process: selecting a population by adopting a roulette algorithm;
step 4, a crossing process: single-point crossing is adopted for chromosome crossing, so that the feasibility of a solution is ensured;
step 5, a mutation process: aiming at the forward construction time sequence and the reverse construction time sequence, a single-point variation rule meeting the construction time sequence constraint is respectively adopted.
10. The electric vehicle ordered charging guidance method based on dynamic demand and new energy consumption according to claim 9, characterized in that: the calculation process of the optimal ordered charging path guidance comprises the following steps:
s1, constructing a multi-user effective path set, and circulating: the travel time T belongs to T, and T is T time periods divided by equal time intervals in one day;
s2, updating travel cost and selecting charging probability according to the state of the charging facility at the last time t-1, dynamically deducing traffic flow, and setting iteration times n =1 for newly increased flow distribution, flow transmission and queuing charging in sequence;
s3, recalculating generalized trip expenses and selection probabilities of all paths of the fuel vehicle and the electric vehicle according to the flow distribution after the last iteration, the charging facility working condition in the network, the road section load and the vehicle state;
s4, carrying out dynamic deduction on the traffic flow to finally obtain an additional flow distribution result;
and S5, calculating the road network flow after the iteration by adopting an iteration weighting method.
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