CN110688743A - Economic charging method for electric bus charging station - Google Patents
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Abstract
The invention discloses an economic charging method for an electric bus charging station, which relates to the field of economic charging methods for electric buses, and comprises the following steps of S1, identifying battery information of each bus based on charging record information of a charging cloud platform according to a bus trip and maintenance scheduling plan, wherein the battery information comprises capacity, charging and discharging characteristic parameters and journey power consumption characteristics; s2, simulating uncertainty of bus departure time by adopting a Monte Carlo method, wherein departure time is randomly distributed in a departure interval before and after planned departure time; and step S3, solving the charging power of each time period of the vehicle by constructing an optimization model according to the following formula A in each Monte Carlo sampling simulation. The invention can optimally calculate and set the upper limit of the state of charge of different periods of time to guide the charging amount of each vehicle in each period of time, and reduce the electricity purchasing cost of the bus station all day.
Description
Technical Field
The invention relates to the field of an electric bus economic charging method, in particular to an electric bus charging station economic charging method.
Background
Buses generally have a relatively fixed driving route and driving time, so that the daily consumed electric energy is relatively stable. In addition, since many public transportation charging stations use a time-of-use electricity rate billing mode for purchasing electricity from a power grid company, there is a great difference in electricity purchase costs for charging at different times. Because the difference of the quantity of the charging piles of the bus charging station, the capacity of the bus battery and the one-way power consumption of the bus cannot guarantee that all the buses are charged at the time with the lowest electricity price and can meet the requirement of running all the day, a proper charging strategy needs to be calculated through an optimization algorithm, the cost of electricity purchasing of the charging station from the power grid all the day is the minimum while the requirement of bus operation traveling is met, and therefore the operation benefit is improved.
The charging starting time and the optimal charging amount of the distributed vehicles can be calculated through optimization under the condition that the departure time and the arrival time of the buses are fixed and the power consumption of each vehicle is fixed, but the departure time and the arrival time of the vehicles always have differences due to differences of road conditions and weather, and theoretical optimization results in actual operation are difficult to apply to practice. In addition, in consideration of cost, the bus charging station may not be equipped with a special operation and maintenance engineer to specially monitor and timely modify the charging strategy of each vehicle, and the vehicle often needs to be charged for a period of time before the vehicle acquires the frame information from the battery management system, so that it is difficult to practically apply the charging strategy for each vehicle, and there is no operability in dynamically modifying the state of charge limit for charging the vehicle on each charging pile.
Based on the reasons, a charge state constraint method for a bus charging station needs to be designed, the method takes the charging station as a unit, can adapt to the deviation between the departure time and the arrival time of a bus and a plan, can set the charge state of the bus and the fitting coefficient and intercept of the charge quantity according to historical orders, and guides the charge quantity of each bus in each time period by optimally calculating and setting the charge state upper limit in different time periods, so that the electricity purchasing cost of the bus charging station is reduced, and the operation benefit is improved.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an economic charging method for an electric bus charging station.
In order to achieve the purpose, the invention adopts the following technical scheme:
an economic charging method for an electric bus charging station comprises the following steps:
step S1, identifying battery information of each bus based on the charging record information of the charging cloud platform according to the bus trip and maintenance scheduling plan, wherein the battery information comprises capacity, charging and discharging characteristic parameters and trip power consumption characteristics;
s2, simulating uncertainty of bus departure time by adopting a Monte Carlo method, wherein departure time is randomly distributed in a departure interval before and after planned departure time;
step S3, in each Monte Carlo sampling simulation, the charging power of each time period of the vehicle is solved by constructing an optimization model according to the following formula A;
formula A:
step S4, after the charge quantity of each time interval is obtained according to the charging power and the charging duration, the initial state of charge value is added, and the optimal state of charge value of each time interval calculated by the optimization can be obtained;
step S5, storing the optimal value of the charge state of each vehicle in each Monte Carlo sampling simulation calculation, simultaneously judging whether the sampling times are met, if not, executing the step S3 again, and if so, entering the step S6;
step S6, after the Monte Carlo sampling simulation calculation is finished, the expected value and deviation of the state of charge of each vehicle in each time period are counted;
step S7, taking the expected value of the state of charge obtained in step S6 as a known condition at the peak moment, and simultaneously taking the state of charge at the departure moment as full charge and the state of charge at the shutdown period as a minimum value, calculating the state of charge at the rest period according to a formula B,
formula B:
step S8, taking the minimum value of the lower limit of the state of charge of each vehicle in each time interval as the lower limit of the state of charge of the charging station charging service in the time interval;
and step S9, when the vehicle is charged in the charging station, the charging equipment requests the state of charge constraint of the charging station to serve as the target upper limit of the vehicle charging from the cloud platform.
Preferably, the optimization model is constructed in step S3 by considering a vehicle battery energy recurrence constraint:
preferably, the vehicle battery energy upper and lower limit constraints are considered when constructing the optimization model in step S3:
SOCmin≤Csoc(i，t)≤SOCmax。
preferably, the vehicle continuous state constraint is considered when constructing the optimization model in step S3:
-Pd(1-SC(i，t))≤Pk(i，t)≤PmaxSC(i，t)-Pd(1-SC(i，t))。
preferably, the optimization model is constructed in step S3 by considering the charging station power constraint:
the invention takes the charging station as a unit, can adapt to the deviation between the departure time and the arrival time of the bus and the plan, can guide the charging amount of each time period of each bus by optimally calculating and setting the upper limit of the charging state of different time periods according to the fitting coefficient and the intercept of the vehicle charging state and the charging electric quantity identified by the historical order, and reduces the all-day electricity purchasing cost of the bus station, thereby improving the economy of the bus charging station and having good application prospect.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, an economic charging method for an electric bus charging station includes the following steps:
step S1, identifying battery information of each bus based on the charging record information of the charging cloud platform according to the bus trip and maintenance scheduling plan, wherein the battery information comprises capacity, charging and discharging characteristic parameters and trip power consumption characteristics;
s2, simulating uncertainty of bus departure time by adopting a Monte Carlo method, wherein departure time is randomly distributed in a departure interval before and after planned departure time;
step S3, in each Monte Carlo sampling simulation, the charging power of each time period of the vehicle is solved by constructing an optimization model according to the following formula A;
formula A:
n buses exist; t is the study period, TtA time duration for each time period;
Pr(t) the electricity price at the time t; sC(i, t) represents a state of charge of the ith vehicle during the t-th period; pk(i, t) is the charging power of the ith vehicle in the t-th period.
Step S4, after the charge quantity of each time interval is obtained according to the charging power and the charging duration, the initial state of charge value is added, and the optimal state of charge value of each time interval calculated by the optimization can be obtained;
step S5, storing the optimal value of the charge state of each vehicle in each Monte Carlo sampling simulation calculation, simultaneously judging whether the sampling times are met, if not, executing the step S3 again, and if so, entering the step S6;
step S6, after the Monte Carlo sampling simulation calculation is finished, the expected value and deviation of the state of charge of each vehicle in each time period are counted;
step S7, taking the expected value of the state of charge obtained in step S6 as a known condition at the peak moment, and simultaneously taking the state of charge at the departure moment as full charge and the state of charge at the shutdown period as a minimum value, calculating the state of charge at the rest period according to a formula B,
formula B:
in the formula Csoc(i, t-1) is the state of charge at the previous moment, known when solving for the state of charge at time t, Csoc(i，ts)、Csoc(i，te) For the start-stop period of charge values obtained in the preceding step, Ei0The power consumption of the vehicle in a single journey is shown, and n is the number of journeys of the vehicle i in the starting and stopping time period.
Step S8, taking the minimum value of the lower limit of the state of charge of each vehicle in each time interval as the lower limit of the state of charge of the charging station charging service in the time interval;
and step S9, when the vehicle is charged in the charging station, the charging equipment requests the state of charge constraint of the charging station to serve as the target upper limit of the vehicle charging from the cloud platform.
In the present embodiment, the vehicle battery energy recurrence constraint is considered when the optimization model is constructed in step S3:
In the present embodiment, the upper and lower energy limit constraints of the vehicle battery are considered when the optimization model is constructed in step S3:
SOCmin≤Csoc(i，t)≤SOCmax. In any time period, the state of charge of the vehicle battery is larger than the lower limit and smaller than the upper limit, the constraint can ensure that the state of charge is not lower than a certain minimum value when the vehicle is dispatched, and the electric vehicle can completely run the whole process.
In the present embodiment, the vehicle continuous state constraint is considered when the optimization model is constructed in step S3:
-Pd(1-SC(i，t))≤Pk(i，t)≤PnaxSC(i，t)-Pd(1-SC(i，t))。
Pdis the average discharge power of the vehicle, PmaxFor upper limit of pile charging power, i-th vehicle is in discharging state during t-th time period (S at the moment)C(i, t) ═ 0), or in a charged state (at this time S)C(i, t) =1), 0 ≦ P in the charged statek(i，t)≤PmaxWhen P iskWhen (i, t) is 0, the vehicle is stopped but not charged.
In this embodiment, the charging station power constraint is considered when the optimization model is constructed in step S3:
. Is the charging station total power limit.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. An economic charging method for an electric bus charging station is characterized by comprising the following steps:
step S1, identifying battery information of each bus based on the charging record information of the charging cloud platform according to the bus trip and maintenance scheduling plan, wherein the battery information comprises capacity, charging and discharging characteristic parameters and trip power consumption characteristics;
s2, simulating uncertainty of bus departure time by adopting a Monte Carlo method, wherein departure time is randomly distributed in a departure interval before and after planned departure time;
step S3, in each Monte Carlo sampling simulation, the charging power of each time period of the vehicle is solved by constructing an optimization model according to the following formula A;
formula A:
step S4, after the charge quantity of each time interval is obtained according to the charging power and the charging duration, the initial state of charge value is added, and the optimal state of charge value of each time interval calculated by the optimization can be obtained;
step S5, storing the optimal value of the charge state of each vehicle in each Monte Carlo sampling simulation calculation, simultaneously judging whether the sampling times are met, if not, executing the step S3 again, and if so, entering the step S6;
step S6, after the Monte Carlo sampling simulation calculation is finished, the expected value and deviation of the state of charge of each vehicle in each time period are counted;
step S7, taking the expected value of the state of charge obtained in step S6 as a known condition at the peak moment, and simultaneously taking the state of charge at the departure moment as full charge and the state of charge at the shutdown period as a minimum value, calculating the state of charge at the rest period according to a formula B,
formula B: Csoc(i，t)＝max(SOCmin)
Step S8, taking the minimum value of the lower limit of the state of charge of each vehicle in each time interval as the lower limit of the state of charge of the charging station charging service in the time interval;
and step S9, when the vehicle is charged in the charging station, the charging equipment requests the state of charge constraint of the charging station to serve as the target upper limit of the vehicle charging from the cloud platform.
2. The economic charging method for an electric bus charging station as claimed in claim 1, wherein the optimization model is constructed in step S3 by considering the vehicle battery energy recurrence constraint:
3. the economic charging method for the electric bus charging station as claimed in claim 2, wherein the optimization model is constructed in step S3 by considering upper and lower energy constraints of the vehicle battery:
SOCmin≤Csoc(i，t)≤SOCmax。
4. the economic charging method for electric bus charging station as claimed in claim 3, wherein the optimization model is constructed in step S3 considering the vehicle continuous state constraint:
-Pd(1-SC(i，t))≤Pk(i，t)≤PmaxSC(i，t)-Pd(1-SC(i，t))。
5. the economic charging method for electric bus charging station as claimed in claim 4, wherein the optimization model is constructed in step S3 by considering the power constraint of charging station:
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| CN112906983A (en) * | 2021-03-22 | 2021-06-04 | 吉林大学 | Electric bus charging scheme optimization method considering time-of-use electricity price influence |
| CN113479103A (en) * | 2021-08-11 | 2021-10-08 | 山东德佑电气股份有限公司 | Charging load optimization method and device of new energy bus charging station |
| CN113673069A (en) * | 2020-05-14 | 2021-11-19 | 南京行者易智能交通科技有限公司 | Design method and device of new energy bus charging model and mobile terminal equipment |
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