CN110570098A - Electric automobile charging and battery replacing station control method considering battery replacing demand and photovoltaic uncertainty - Google Patents
Electric automobile charging and battery replacing station control method considering battery replacing demand and photovoltaic uncertainty Download PDFInfo
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Abstract
An electric vehicle charging and battery replacing station control method considering battery replacing requirements and photovoltaic uncertainty comprises the following steps: establishing a current control mathematical model of the power station based on opportunity constraint planning; performing deterministic conversion on opportunity constraints of power change demand and opportunity constraints of daily electricity purchase cost of the power change station based on the probabilistic sequence; risk index and punishment cost of current control of the power station day-ahead include: risk indexes and cost underestimation risk indexes which do not meet the battery replacement demand and penalty costs corresponding to control risks in the day-ahead; and fast optimizing a current switching station day-ahead control mathematical model based on the determined feasible solution space, wherein the fast optimizing comprises determining a current switching station battery pack load margin, and solving the current switching station day-ahead control mathematical model through the current switching station battery pack load margin based on a genetic algorithm. The method and the system can provide reference for reasonable decision of the power change station operator, are beneficial to the optimized operation of the power change station, and promote the further popularization of the electric automobile and the power change station.
Description
Technical Field
The invention relates to a day-ahead control method for a power changing station. In particular to a control method of an electric automobile charging and replacing station considering the battery replacing requirement and photovoltaic uncertainty, which is suitable for the electric automobile charging and replacing station.
background
as an important solution to the urban environmental problem, electric vehicles have recently received wide attention from governments, academic circles and industrial circles of various countries in the world, and have been vigorously popularized. In the 7-month twenty-national group (G20) hamburger meeting period in 2017, in order to promote the realization of Paris agreement, European countries such as Law, De, Norway, the Netherlands and the like declare that fuel automobiles are forbidden to be sold comprehensively in 2025-2040 years respectively, which greatly promotes the further promotion and improvement of related technologies such as power batteries and the like, thereby promoting the comprehensive application of electric automobiles. For buses and taxis with running time far longer than parking time and more easily unified vehicle types and battery models, the battery replacement mode not only can realize quick energy supply of electric vehicles, but also can eliminate peak-valley difference of charging loads, assist power grid frequency adjustment, reduce energy consumption cost and improve asset utilization efficiency through ordered charging management, thereby becoming the most efficient alternative mode. Meanwhile, facilities for realizing the battery replacement mode usually occupy a large area, and if large-scale photovoltaic power generation can be arranged according to local conditions, the clean energy occupation ratio in the primary energy of the electric vehicle can be more effectively improved, and the power supply burden of a power grid can be reasonably reduced. Obviously, the day-ahead charging control of the photovoltaic-electric vehicle charging and replacing station plays an important supporting role in realizing the above effects.
At present, some scholars research the day-ahead charging control problem of the photovoltaic charging and replacing power station considering uncertainty. The complexity of the charging control problem of the photovoltaic power conversion station is that the photovoltaic output at the source side and the power conversion demand at the charge side have great uncertainty. However, the existing research has not been completely considered for the uncertainty problem, depends on prediction or adopts Monte Carlo simulation and multi-scenario technology to process the uncertainty factor, has limited coverage for the uncertainty situation in actual operation, and is rarely related to the day-ahead control model which considers the uncertainty in the two aspects.
Disclosure of Invention
The invention aims to solve the technical problem of providing a control method for an electric vehicle charging and battery replacing station, which is beneficial to the optimized operation of the battery replacing station and takes the battery replacing requirement and photovoltaic uncertainty into consideration.
The technical scheme adopted by the invention is as follows: a control method for an electric vehicle charging and battery replacing station considering battery replacing requirements and photovoltaic uncertainty comprises the following steps:
1) Establishing a current control mathematical model of the power station based on opportunity constraint planning;
2) Performing deterministic conversion on opportunity constraints of power change demand and opportunity constraints of daily electricity purchase cost of the power change station based on the probabilistic sequence;
3) Risk index and punishment cost of current control of the power station day-ahead include: risk indexes and cost underestimation risk indexes which do not meet the battery replacement demand and penalty costs corresponding to control risks in the day-ahead;
4) And fast optimizing a current switching station day-ahead control mathematical model based on the determined feasible solution space, wherein the fast optimizing comprises determining a current switching station battery pack load margin, and solving the current switching station day-ahead control mathematical model through the current switching station battery pack load margin based on a genetic algorithm.
According to the electric vehicle charging and switching station control method considering the switching demand and the photovoltaic uncertainty, the double uncertainties of the switching demand and the photovoltaic output in the operation of the switching station can be considered, the opportunity constraint planning model is utilized to enable the charging and switching operation scheme formulated in the day ahead to achieve the optimized operation of the switching station under a certain confidence level, and the risk cost existing in the day ahead control process is measured. The method and the system can provide reference for reasonable decision of the power change station operator, are beneficial to the optimized operation of the power change station, and promote the further popularization of the electric automobile and the power change station.
drawings
FIG. 1 is a schematic time of use electricity prices employed in embodiments of the present invention;
FIG. 2 is a schematic of a photovoltaic output probabilistic sequence in an embodiment of the invention;
FIG. 3 is a schematic diagram of a probabilistic sequence of power swapping requirements according to an embodiment of the invention;
FIG. 4 is a schematic diagram of a comparison of costs of a charging schedule in an embodiment of the present invention;
FIG. 5 is a schematic illustration of a charging schedule at different confidence levels in an embodiment of the present invention;
Fig. 6 is a schematic diagram of a probabilistic sequence of power purchase cost for each time period during the day according to an embodiment of the present invention.
Detailed Description
The following describes in detail a control method of an electric vehicle charging and battery replacing station considering battery replacing requirements and photovoltaic uncertainty with reference to an embodiment and an attached drawing.
The invention discloses a control method for an electric automobile charging and battery replacing station considering battery replacing requirements and photovoltaic uncertainty, which comprises the following steps of:
1) establishing a current control mathematical model of the power station based on opportunity constraint planning;
the current-station day-ahead control mathematical model comprises a target function which is used for enabling the charge of the battery pack of the current-station accessing to the power grid to be minimum, and constraint conditions which are used for constraint of current-changing demand opportunity, constraint of opportunity of current purchasing cost of the current-station day and constraint of certainty of charging load of the current-station. Wherein
(1) the expression of the objective function minF is as follows:
wherein the content of the first and second substances,
in the formula, PEL(t) is the equivalent charging load; pb(t) charging load of a battery pack in the power station is changed in a time period t, namely a decision variable controlled by the power station day ahead; pPV(t) is the photovoltaic output power in the period of t, and is a random variable; p (t) is the time-of-use electricity price of the power grid;
(2) The power conversion demand opportunity constraint expression is as follows:
In the actual operation process of the battery replacement station, the battery replacement requirement has randomness, and in order to enable the probability that the actual battery replacement requirement is met not to be lower than a given confidence level, the following battery replacement requirement constraints are set:
Pr{Nneed(t)≤Ns(t)}≥α (3)
In the formula, Pr{. represents the probability that the event holds; n is a radical ofneed(t) is the actual power change demand in the time period t, and is a random variable; n is a radical ofs(t) the number of battery packs for power change is provided for a power change station plan in a time period t, and alpha is a power change requirement confidence level set by a power change station operator;
(3) the opportunity constraint expression of the daily electricity purchasing cost of the electricity changing station is as follows:
under the influence of photovoltaic output day-ahead prediction errors, the cost generated in actual operation of the control scheme formulated day-ahead has volatility, and in order to minimize the electricity purchasing cost under the constraint of the confidence level of the control scheme formulated day-ahead, the following opportunity constraint of the electricity purchasing cost of the electricity changing station day is set:
In the formula, f (P)b,PPV) Is an objective function; pbThe decision vector is the load for charging the battery pack in each time period controlled and made by the power station day ahead; pPVIs a random vector; f is the objective function f (P)b,PPV) The minimum value is obtained when the confidence level is not lower than beta, wherein beta is the daily electricity purchase cost confidence level set by the operator of the power station;
(4) the charging load certainty constraint expression of the battery replacement station is as follows:
Pmin(t)≤Pb(t)≤min{Pmax(t),PM} (5)
In the formula, Pmin(t)、Pmax(t) the upper and lower limits of the battery pack charging load margin of the battery replacing station after the battery replacing requirement is considered; pb(t) charging load of a battery pack in the power station is changed in a time period t, namely a decision variable controlled by the power station day ahead; pMThe maximum charging power is limited by the number of chargers for the photovoltaic power changing station; the upper and lower limits of the charging load margin of the battery pack of the battery changing station are combined with the limit of the number of chargers of the battery changing station to serve as the deterministic constraint of the charging load of the battery changing station.
2) Performing deterministic conversion on opportunity constraints of power change demand and opportunity constraints of daily electricity purchase cost of the power change station based on the probabilistic sequence; the method comprises the following steps:
(1) Respectively representing random variables in the current control mathematical model of the power station by using probabilistic sequences; the method comprises the following steps:
(1.1) representing the actual power change demand N in the t period by a probabilistic sequenceneed(t)：
due to the randomness of the Monte Carlo simulation and the fluctuation of the actual battery replacement requirement, the method takes the result obtained by the Monte Carlo simulation as the mean value Nneed,tconstructing an uncertainty model N (N) of the battery replacement requirements in each time periodneed,t,σneed,t 2) First, the probability density function f of the battery swapping requirementneed,t(x) The expression is as follows:
Wherein σneed,tThe standard deviation of the uncertainty model of the battery replacement requirement is shown; x is the battery replacement requirement;
constructing a power change demand time sequence multi-state probabilistic sequence by using probability density functions of each time period of the power change demand and recording the sequence as Nneed,t(iNt) Probabilistic sequence of power swapping requirements Nneed,t(iNt) Length N ofNneed,t＝Nneed,tmax，Nneed,tmaxThe maximum value of the battery replacement requirement in the time period t is obtained; calculating the probabilistic sequence N of the actual battery replacement demandneed,t(iNt) The expression is as follows:
(1.2) representing the photovoltaic output power P in the t period by a probabilistic sequencePV(t)：
The photovoltaic output actual value is represented by the sum of the short-term predicted value of the photovoltaic output and the prediction error, and the expression is as follows:
PPVr(t)＝PPVf(t)+e(t) (8)
Wherein, PPVr(t) and PPVf(t) respectively representing the actual photovoltaic output value and the predicted photovoltaic output value in the t period; e (t) isPrediction error, using normal distribution of N (0, σ)PV,t 2) To express, standard deviation σPV,ttaken as the predicted value PPVf10% of (t); photovoltaic output power P at t periodPV(t) probability density function fPV,t(y) the expression is:
Constructing a photovoltaic output probabilistic sequence by utilizing the probability density function of each photovoltaic output time interval, and recording as PPV,t(iPVt) Length of sequence NPV,tthe expression is as follows:
In the formula (I), the compound is shown in the specification,Is not more thanthe largest integer of (a); pPV,tmaxThe maximum value of photovoltaic output possible in the time period t; Δ P is the discretization step length; calculating a photovoltaic output probabilistic sequence PPV,t(iPVt) The expression is as follows:
(2) performing sequence operation on the target function;
The sequence operation is carried out on the target function, and the expression is as follows:
wherein, PEL,t(iELt) For a probabilistic sequence of equivalent charging loads in a t-slot charging station, PEL,t(iELt) Has a length of NEL,t，iELtFor equivalent charging in the power stationProbabilistic sequence of loads PEL,t(iELt) The serial number of (a); pb,t(ibt) Unit sequence of charging loads for battery packs of a t-slot converter station, Pb,t(ibt) Length Nb,t，ibtUnit sequence P for charging load of battery pack of battery changing stationb,t(ibt) The serial number of (a); pPV,t(iPVt) Is a time-sequential probabilistic sequence of photovoltaic contribution at time t, PPV,t(iPVt) Has a length of NPV,t，iPVtFor a time-series probabilistic sequence P of photovoltaic outputsPV,t(iPVt) The serial number of (a);
When i isELtWhen P is 0EL,t(0) The sum of the probabilities that all charging loads are less than or equal to the photovoltaic output condition is obtained, namely the negative value part in the difference of the charging loads and the photovoltaic output condition is combined into PEL,t(0) The volume difference operation has actual physical significance, namely the electric quantity charged by the accessed power grid is 0 at the moment, and the actual situation is met;
And photovoltaic output does not exist at night, so the photovoltaic output probabilistic sequence at night is a zero sequence, and the result of performing the rolling difference operation on the battery pack charging load sequence and the photovoltaic output probabilistic sequence of the power change station is still the power change station charging load sequence.
Let the probabilistic sequence of the electricity purchasing cost of the t-period power change station be Ct(iCt) Sequence length NCtsequence number is iCtRemember pt(ipt) Is a unit sequence of time-of-use electricity price, and the sequence length is Npt，Sequence number iptΔ p is the discretization step length of the time-of-use electricity price; then C ist(iCt) From PEL,t(iELt) And pt(ipt) The sequence multiplication calculation obtains:calculating a probabilistic sequence C of electricity purchasing cost of the electricity changing station according to the definition of the sequence multiplication operationt(iCt) The expression is:
wherein iCt＝0,1,…,NCt，NCt＝NEL,t·Npt；
the probabilistic sequence of the total electricity purchasing cost of the power station is F (i)F) Sequence length NFsequence number is iF，F(iF) Probabilistic sequence C of electricity purchase costs from a 24-time-slot power stationt(iCt) Volume and calculation yields:
Wherein iF＝0,1,…,NF，
(3) If the chance constraint is converted into a deterministic form, the equations (3) and (4) contain random variables, and if the chance constraint is converted into a deterministic constraint equivalent form, an inverse function of the cumulative distribution of the random variables must be obtained, and the probability density function form of the random variables is complex, and the daily electricity purchase cost of the power station is complex to calculate, so the inverse function of the cumulative distribution is difficult to obtain.
The probability distribution of random variables is discretized by the probabilistic sequence operation method, and a complex calculation result containing the random variables is obtained, so that the probability distribution of the daily electricity purchase cost is discretized, and the opportunity constraint can be converted into deterministic constraint processing.
the conversion of the opportunity constraint into a deterministic form is: probabilistic sequence N based on constructed swapping requirementsneed,t(iNt) Dividing the possible power conversion requirement in the t period into NNneed,t+1 states, NNneed,tProbabilistic sequence N for power swap requirementsneed,t(iNt) Length of (2), sequence number iNt＝0,1,…,NNneed,tDirectly calculating the cumulative probability of the power change requirements corresponding to each state of the power change requirements in the t period; let Ns(t) providing the minimum value of the number of the battery replacing sets for the battery replacing station required by meeting the opportunity constraint in the time period t, and then Ns(t) sequence number i of corresponding state in probabilistic sequenceNstsatisfy P (i)Nt≤iNst-1) < alpha and P (i)Nt≤iNst) The alpha is a swapping requirement confidence level set by a swapping station operator; accordingly, N satisfying the chance constraint expression (3) of battery replacement requirement is obtaineds(t), namely, the number of battery packs for providing replacement power according to the replacement power station plan under the corresponding confidence level alpha;
according to a probabilistic sequence F (i) of the constructed electricity change station electricity purchase cost all dayF) Dividing the possible all-day electricity purchasing cost of the t period into NF+1 states, NFProbabilistic sequence F (i) for electricity cost purchase throughout the dayF) Length of (2), sequence number iF＝0,1,…,NFDirectly calculating the cumulative probability of the electricity purchasing cost in all days corresponding to each state; is provided withif the t period is the minimum value taken when the opportunity constraint is satisfied, thenSequence number i of corresponding state in probabilistic sequencefSatisfy P (i)F≤if-1) < beta and P (i)F≤if) Beta is more than or equal to beta, and beta is a daily electricity purchasing cost confidence level set by an operator of the electricity changing station; and f meeting the chance constraint expression (4) of the daily electricity purchasing cost of the electricity exchanging station is obtained, namely the minimum value of the all-day electricity purchasing cost of the electricity exchanging station under the daily electricity purchasing cost confidence level beta set by the operator of the electricity exchanging station.
3) Risk index and punishment cost of daily control of the power station,
Because the opportunity constraint in the current power station day-ahead control mathematical model is satisfied in a probability form, a certain probability is inevitably not satisfied, that is, there is a risk that the current power station electricity purchasing cost is underestimated and the current power demand cannot be satisfied due to the decision made by day-ahead control. In order to more comprehensively show the economical efficiency and the risk of decisions made by the day-ahead control to the power station changing operators, the risk indexes of the day-ahead control need to be quantified, and the corresponding penalty cost is calculated. Risk index and punishment cost of power station day-ahead control, risk index and punishment cost of power station day-ahead control include: risk indexes and cost underestimation risk indexes which do not meet the battery replacement demand and penalty costs corresponding to control risks in the day-ahead; wherein the content of the first and second substances,
(1) risk index that battery replacement demand is not satisfied
The power swapping requirement opportunity constraint expression (3) is satisfied in a probability form, uncertainty of actual power swapping requirements is considered, a certain probability exists that the opportunity constraint is not satisfied, namely, the power swapping requirement amount is more than the number of battery packs for power swapping provided by the power swapping station, and the power swapping requirements of part of users cannot be satisfied. This situation may reduce the user satisfaction of the power swapping station, which is undesirable for the power swapping station operator. Therefore, the method quantifies the risk that the power change demand is not met, and calculates the corresponding penalty cost.
Meanwhile, the battery replacement demand may be lower than the number of battery packs for providing battery replacement by the battery replacement station. In order to realize the economic operation of the battery replacement station, the battery replacement station needs to operate according to a control scheme formulated before the day, so that on the premise of considering the satisfaction degree of users, the following battery replacement rule for the actual operation of the battery replacement station is formulated at first:
Rolling and updating the number of battery packs providing battery replacement in the battery replacement station according to the actual battery replacement requirement, and transferring the remaining full-battery packs in the battery replacement station in the current time period to the battery pack reserve providing battery replacement in the next time period if the battery replacement requirement in the previous time period is lower than the number of battery packs providing battery replacement in the battery replacement station; if the battery replacement demand is higher than the number of battery packs for providing battery replacement by the battery replacement station in the current time period, the part of the battery packs for providing battery replacement by the battery replacement station in the current time period is not replaced,
With Ns' (t) represents the updated number of battery packs for providing replacement power by the replacement power station, and the expression is as follows:
Wherein N isneed(t) is the actual battery replacement demand in the time period t, Ns(t +1) providing the number of battery packs for power conversion for the power conversion station plan in the t +1 time period;
Number D of electric vehicles incapable of changing power in t periodNS(t) the expression is:
Since the battery change requirement is a random variable, DNS(t) is also a random variable, and the invention uses the expected value E (D)NS(t)) represents the electricity switching demand which is not met in the period of t, quantifies the risk index which is not met by the electricity switching demand, and the punishment cost C corresponding to the risk index which is not met by the electricity switching demandDNSThe expression is as follows:
In the formula, λdnsUnit penalties for the power change station failing to meet the power change demand;
Recording a probabilistic sequence corresponding to the number of battery packs for providing battery replacement by the battery replacement station within the updated t-period as N 'according to the established actual operation rule of the battery replacement station'St(i'st) Of sequence length N'Nstthe sequence number is i'stRecording a probabilistic sequence corresponding to the number of remaining full-battery packs in the power station during the time period t as Δ Nt'(it) Sequence length NΔNtsequence number itThen Δ Nt'(it) From N'St(i'st) And Nneed,t(iNt) The volume difference calculation yields:
ΔNt'(it)＝N'St(i'st)ΘNneed,t(iNt) (18)
Wherein it＝0,1,…,NΔNt，NΔNt＝N'Nst；Nneed,t(iNt) A probabilistic sequence of power change requirements with sequence number iNt，NNneed,tProbabilistic sequence N for power swap requirementsneed,t(iNt) The length of (a) of (b),
Let NS(t+1)(is(t+1)) A probabilistic sequence corresponding to the number of battery groups for providing power conversion for the original plan of the power conversion station in the time period of t +1, wherein the sequence length is NNs(t+1)Sequence number is(t+1)the probabilistic sequence corresponding to the number of battery packs for providing power swapping by the power swapping station updated in the period of t +1 is N'S(t+1)(i's(t+1)) Of sequence length N'Ns(t+1)The sequence number is i's(t+1)From NS(t+1)(is(t+1)) And Δ Nt'(it) Volume and calculation yields:
Wherein, i's(t+1)＝0,1,…,N'Ns(t+1)，N'Ns(t+1)＝NNs(t+1)+NΔNt；
Recording a probabilistic sequence of the battery replacement demand which is not met in the period t as DNSt(iDt) Sequence length NDtsequence number iDtthen, then
DNSt(iDt)＝Nneed,t(iNt)ΘN'St(i'st) (20)
In the formula iDt＝0,1,…,NDt，NDt＝NNneed,tExpected value E (D) of the battery replacement demand that is not satisfied in the period tNS(t)) is:
Substituting penalty cost C corresponding to risk indexDNSAn expression (17) is used for solving a punishment cost corresponding to the risk that the battery replacement demand is not met;
(2) Said cost underestimation risk indicator
Under the influence of photovoltaic output day-ahead prediction errors, the electricity purchasing cost is minimized by a control scheme formulated day-ahead under the constraint of a confidence level, and an opportunity constraint expression (4) that the electricity purchasing cost per day of the power station cannot be met with a certain probability inevitably exists, so that the risk of underestimation of the electricity purchasing cost exists. Quantifying the cost underestimation by using the risk index, and setting the penalty cost C of the cost underestimationunderthe expression is as follows:
Cunder＝E(funder)·λunder (22)
wherein, E (f)under) To cost-underestimate the expected value of the part, λunderUnit penalty for underestimating the control cost of the power station day ahead;
The sequence of the cost-underestimated part is denoted as funder(iF) Sequence number iFThen the expression is:
Wherein: delta P is the discretization step length of photovoltaic output; Δ p is the discretization step of the time of use electricity price,The minimum value of the objective function is taken when the confidence level is not lower than beta, wherein beta is the confidence level of the daily electricity purchasing cost set by the operator of the power station, and the expected value expression of the cost underestimation part is as follows:
Penalty cost C for substitution cost underestimationunderAn expression (22) for solving a penalty cost corresponding to the cost underestimation;
(3) Penalty cost corresponding to the day-ahead control risk
the risk of making decisions by the power station day-ahead control is determined by both a random variable and a confidence level selected by the power station operator. Penalty cost C corresponding to day-ahead control riskriskPenalty cost C underestimated by costunderPunishment cost C corresponding to risk index not meeting battery replacement requirementDNSThe expression is as follows:
Crisk＝CDNS+Cunder (25)。
4) and fast optimizing a current switching station day-ahead control mathematical model based on the determined feasible solution space, wherein the fast optimizing comprises determining a current switching station battery pack load margin, and solving the current switching station day-ahead control mathematical model through the current switching station battery pack load margin based on a genetic algorithm. Wherein
the method for determining the battery pack load margin of the battery changing station comprises the following steps of according to the state parameters of the battery packs in the battery changing station, representing the state of the battery packs through a three-dimensional row vector, and establishing a state matrix of the battery packs as follows:
Status＝(n,SocN,Ts) (26)
In the formula, n is the current charging state identifier of the battery pack:
SocN represents the current state of charge; t issindicating the time when the battery pack is changed, namely the starting time of charging the battery pack;
according to the battery pack state matrix, the controlled states of the standby battery pack in the time period switching station are divided into the following 4 states: the number of battery packs being charged is N1(t) the number of battery packs whose charging must be stopped is N2(t) the number of fully charged battery packs is N3(t) and the number of battery packs to be charged is N4(t); the first three battery packs are all in an uncontrollable state; in order to meet the power change requirement at the subsequent time, a part of battery packs to be charged at each time must be connected to a power grid for charging, the part of battery packs are in an uncontrollable state, and the number of the battery packs is N4-1(t), the rest of the batteries to be charged are in a controllable state, and the number of the batteries to be charged is N4-2(t)；
according to the definition of various states of the battery pack, 4 state vectors corresponding to the battery pack with various states are represented as follows:
Therein, SOCmaxrepresents the battery pack full state charge amount;
the battery pack load margin in the t +1 time period is influenced by the battery pack charging load in the t time period and the battery replacement requirement in the t +1 time period, and battery pack state information in the battery replacement station in the t time period and the t +1 time period is calculated through a battery pack state information calculation expression:
Wherein: n is a radical ofleft(t) represents the number of battery packs remaining to be charged for a period of t; n is a radical ofneed(t +1) is the battery replacement requirement obtained through Monte Carlo simulation; pmin(t)、Pmax(t) the upper and lower limits of the battery pack charging load margin of the battery replacing station after the battery replacing requirement is considered; pb(t) charging load of a battery pack in the power station is changed in a time period t, namely a decision variable controlled by the power station day ahead; pratedaverage charging power for the battery pack;
considering the uncertainty of the power change requirement, in order to ensure that the probability of meeting the actual power change requirement is not lower than the power change requirement confidence level alpha set by the power change station operator, the power change unit number N is provided by the power change plan in the time period of t +1S(t +1) alternative battery state information estimation expression (28) for Nneed(t+1)。
the invention adopts a real number coded genetic algorithm and combines sequence operation to optimize the battery pack charging plan of the power change station, thereby obtaining the minimum electricity purchasing cost of the power change station which meets the opportunity constraint under the condition of considering uncertainty. The method for solving the current control mathematical model of the power change station through the battery pack load margin based on the genetic algorithm comprises the following steps:
(1) Selecting decision variables of genetic algorithms
As can be known from the battery pack state information calculation expression (28), the upper and lower limits of the battery pack load margin in the battery replacement station are recurred by the influence of the battery pack charging load of the battery replacement station at the previous moment, and the P of the whole dayband (t) is just a decision variable in an economic operation model of the battery replacement station, and the feasible solution range of the battery pack charging plan is changed and is inconvenient for direct solution. Therefore, the decision variables in the genetic algorithm are set to 24 [0,1 ]]represents the charging load P of the battery pack in the power conversion stationb(t +1) is located in the feasible solution space, then
Pb(t+1)＝Pmin(t+1)+x(t)·(min{Pmax(t+1),PM}-Pmin(t+1)) (29)
Wherein, Pmin(t+1)、Pmax(t +1) considering the upper and lower limits of the battery pack charging load margin of the battery replacing station after the battery replacing requirement is considered in the time period of t + 1; pMthe maximum charging power is limited by the number of chargers for the photovoltaic power changing station;
(2) Setting fitness function of genetic algorithm
each individual in the population randomly generated by the genetic algorithm represents a different charging plan, and the power purchase cost probabilistic sequence F (i) corresponding to each charging plan is calculatedF) And obtaining a fitness function of 1/minF by a target function minF which enables the battery pack of the battery changing station to be accessed into the power grid for charging and has the minimum cost;
(3) And substituting the decision variable x (t) and the fitness function 1/minF into a genetic algorithm to obtain the optimal solution of the current switching station day-ahead control mathematical model.
The following preferred embodiments are given:
(1) Typical scenarios and parameter settings
the invention assumes that 1000 electric vehicles adopting a battery replacement mode exist in a service area of a battery replacement station, the number of battery packs in the battery replacement station is 600, and the number of chargers is 350. The average charging power of the electric automobile is 5kW, the battery capacity is 50 kW.h, the lowest charge capacity of the battery pack is 20%, and the highest charge capacity of the battery pack is 90%.
The time-of-use electricity price of electricity purchased by the electricity changing station from the power grid is shown in fig. 1, the photovoltaic installed capacity of the electricity changing station is 240kW, the photovoltaic output data of typical days in summer is used as a photovoltaic prediction parameter, the discretization step size is 5kW, and a photovoltaic output time sequence multi-state probabilistic sequence is constructed and is shown in fig. 2.
a power conversion demand time sequence multi-state probabilistic sequence constructed by taking data obtained from the monte carlo simulation as a mean value is shown in fig. 3.
(2) Results and analysis
In order to prove the scientificity and the validity of the method for processing the uncertain factors, the charging plan formulated by the method is compared with the charging plan formulated by adopting the deterministic photovoltaic output predicted value and the Monte Carlo simulation battery replacement requirement, and the electricity purchasing cost F and the punishment cost C are respectively selectedriskCost F', F ═ F + C associated with risk that opportunity constraints are not satisfiedriskThese aspects were analyzed in comparison. The confidence level alpha is set to be 0.8, and the confidence level beta is set to be 0.9, and the method of the invention is compared with a power station economic operation scheme which is made by a method without considering uncertainty, for example, as shown in fig. 4.
As can be seen from fig. 4, the economic operation charging plan of the battery replacement station, which is made without considering the uncertainty, is slightly lower than the charging plan made by the method of the present invention in terms of electricity purchasing cost, but the penalty cost is much higher than the penalty cost corresponding to the charging plan of the method of the present invention. The cost obtained by the method of the invention after considering the risk is more economical.
On the basis of a certain user satisfaction requirement, the charging station operator can set different confidence levels beta according to self risk preference in consideration of uncertainty of photovoltaic output, and accordingly a corresponding charging plan of the charging station is formulated in the day ahead.
the charging demand uncertainty model is constructed by using the charging demand obtained by Monte Carlo simulation as a mean value model, and the charging plans formulated when beta is different values are respectively shown in FIG. 5.
As can be seen from fig. 5, when the confidence level that the power swapping requirement is met is fixed, the charging plan made when β is large is compared with the charging plan made when β is small, the charging peak is advanced by a time interval at 7, and the charging peak is delayed by a time interval around 18, so that the charging load of the battery pack at the daytime time interval is correspondingly reduced, the influence of the uncertainty of photovoltaic output on day-ahead control is further reduced, and the confidence level of day-ahead control is improved.
charging schedules at different confidence levels can react sensitively to changes in the hourly electricity prices, and each charging schedule charges the battery in advance to different degrees in the time periods before the increase of the hourly electricity prices at 7 o and 18 o, so that the number of the charged batteries is correspondingly reduced in the time period after the increase of the hourly electricity prices. In addition, although the economical charging plan reserves the battery charge in advance in the low-price period, different charging load peaks inevitably occur in the high-price periods around 12 hours and 18 hours because the battery replacement demand is in the peak period. The charging load peak at 12 is lower than the charging load peak around 18 because the charging schedule reserves the battery charge ahead of time at 12 in the lower price period.
in order to analyze the influence of the photovoltaic output uncertainty on the charging cost, a charging plan formulated when β is 0.95 in fig. 5 is selected, the uncertainty is processed by a probabilistic sequence operation method in the present invention, a charging cost probabilistic sequence of each time period related to the photovoltaic output during the day is obtained, and a probability density map of the charging cost probabilistic sequence of each time period is drawn, as shown in fig. 6.
as can be seen from fig. 6: in a 11-15 time period with a large photovoltaic output mean value, the uncertainty of the photovoltaic output is large, the possible value range of the charging cost in the time period is wider, and the uncertainty is large, wherein in 14 time period, because the set charging load of the battery replacement station is low, the photovoltaic output can completely bear the charging load of the battery at a certain probability, the battery replacement station does not need to purchase electricity to a power grid, and the possibility that the charging cost in the time period is 0 is large; on the contrary, in a time period with a small photovoltaic output average value, the possible value range of the charging cost in the time period is narrower, and the cost fluctuation is smaller.
the method considers the influence of the power conversion demand and the uncertainty of the photovoltaic output on the day-ahead control of the power conversion station, adopts a probabilistic sequence to describe random variables, and introduces opportunity constraint planning which has advantages in processing the uncertainty problem into an economic operation model of the power conversion station. Risks brought by the confidence level selection in the actual operation process of the power change station in the opportunity constraint planning are analyzed, and the confidence level can be selected by an operator of the power change station according to the risk preference of the operator, so that the method and the system provide reference for the selection of the confidence level.
Claims (10)
1. A control method for an electric vehicle charging and battery replacing station considering battery replacing requirements and photovoltaic uncertainty is characterized by comprising the following steps:
1) establishing a current control mathematical model of the power station based on opportunity constraint planning;
2) Performing deterministic conversion on opportunity constraints of power change demand and opportunity constraints of daily electricity purchase cost of the power change station based on the probabilistic sequence;
3) Risk index and punishment cost of current control of the power station day-ahead include: risk indexes and cost underestimation risk indexes which do not meet the battery replacement demand and penalty costs corresponding to control risks in the day-ahead;
4) And fast optimizing a current switching station day-ahead control mathematical model based on the determined feasible solution space, wherein the fast optimizing comprises determining a current switching station battery pack load margin, and solving the current switching station day-ahead control mathematical model through the current switching station battery pack load margin based on a genetic algorithm.
2. The electric vehicle charging and replacing station control method considering the battery replacing demand and the photovoltaic uncertainty as claimed in claim 1, wherein the battery replacing station day-ahead control mathematical model in step 1) includes a target function of minimizing the charge of the battery pack of the battery replacing station accessing to the power grid, and a constraint condition of an opportunity constraint of the battery replacing demand, an opportunity constraint of a daily electricity purchasing cost of the battery replacing station, and a constraint of certainty of a charging load of the battery replacing station.
3. the method for controlling the electric vehicle charging and replacing power station considering the battery replacing demand and the photovoltaic uncertainty as claimed in claim 2,
(1) The expression of the objective function minF is as follows:
wherein the content of the first and second substances,
In the formula, PEL(t) is the equivalent charging load; pb(t) charging load of a battery pack in the power station is changed in a time period t, namely a decision variable controlled by the power station day ahead; pPV(t) is the photovoltaic output power in the period of t, and is a random variable; p (t) is the time-of-use electricity price of the power grid;
(2) The power conversion demand opportunity constraint expression is as follows:
Pr{Nneed(t)≤Ns(t)}≥α (3)
In the formula, Pr{. represents the probability that the event holds; n is a radical ofneed(t) is the actual power change demand in the time period t, and is a random variable; n is a radical ofs(t) the number of battery packs for power change is provided for a power change station plan in a time period t, and alpha is a power change requirement confidence level set by a power change station operator;
(3) The opportunity constraint expression of the daily electricity purchasing cost of the electricity changing station is as follows:
in the formula, f (P)b,PPV) Is an objective function; pbThe decision vector is the load for charging the battery pack in each time period controlled and made by the power station day ahead; pPVIs a random vector;as an objective function f (P)b,PPV) The minimum value is obtained when the confidence level is not lower than beta, wherein beta is the daily electricity purchase cost confidence level set by the operator of the power station;
(4) The charging load certainty constraint expression of the battery replacement station is as follows:
Pmin(t)≤Pb(t)≤min{Pmax(t),PM} (5)
In the formula, Pmin(t)、Pmax(t) isConsidering the upper and lower limits of the battery pack charging load margin of the battery replacing station after the battery replacing requirement is considered; pb(t) charging load of a battery pack in the power station is changed in a time period t, namely a decision variable controlled by the power station day ahead; pMThe maximum charging power is limited by the number of chargers for the photovoltaic power changing station; the upper and lower limits of the charging load margin of the battery pack of the battery changing station are combined with the limit of the number of chargers of the battery changing station to serve as the deterministic constraint of the charging load of the battery changing station.
4. The electric vehicle charging and replacing power station control method considering the battery replacing demand and the photovoltaic uncertainty as recited in claim 1, wherein the step 2) comprises:
(1) Respectively representing random variables in the current control mathematical model of the power station by using probabilistic sequences;
(2) Performing sequence operation on the target function;
(3) the opportunity constraint is converted into a deterministic form.
5. The method for controlling the electric vehicle charging and replacing power station considering the battery replacing demand and the photovoltaic uncertainty as claimed in claim 5, wherein the step (1) comprises:
(1.1) representing the actual power change demand N in the t period by a probabilistic sequenceneed(t)：
Taking the result obtained by Monte Carlo simulation as the mean value Nneed,tConstructing an uncertainty model N (N) of the battery replacement requirements in each time periodneed,t,σneed,t 2) First, the probability density function f of the battery swapping requirementneed,t(x) The expression is as follows:
Wherein σneed,tThe standard deviation of the uncertainty model of the battery replacement requirement is shown; x is the battery replacement requirement;
Constructing a power change demand time sequence multi-state probabilistic sequence by using probability density functions of each time period of the power change demand and recording the sequence as Nneed,t(iNt) Probabilistic sequence of power swapping requirements Nneed,t(iNt) Length N ofNneed,t＝Nneed,tmax，Nneed,tmaxThe maximum value of the battery replacement requirement in the time period t is obtained; calculating the probabilistic sequence N of the actual battery replacement demandneed,t(iNt) The expression is as follows:
(1.2) representing the photovoltaic output power P in the t period by a probabilistic sequencePV(t)：
The photovoltaic output actual value is represented by the sum of the short-term predicted value of the photovoltaic output and the prediction error, and the expression is as follows:
PPVr(t)＝PPVf(t)+e(t) (8)
Wherein, PPVr(t) and PPVf(t) respectively representing the actual photovoltaic output value and the predicted photovoltaic output value in the t period; e (t) is the prediction error, and is normally distributed N (0, sigma)PV,t 2) To express, standard deviation σPV,ttaken as the predicted value PPVf10% of (t); photovoltaic output power P at t periodPV(t) probability density function fPV,t(y) the expression is:
Constructing a photovoltaic output probabilistic sequence by utilizing the probability density function of each photovoltaic output time interval, and recording as PPV,t(iPVt) Length of sequence NPV,tThe expression is as follows:
In the formula (I), the compound is shown in the specification,is not more thanThe largest integer of (a); pPV,tmaxThe maximum value of photovoltaic output possible in the time period t; Δ P is the discretization step length; calculating a photovoltaic output probabilistic sequence PPV,t(iPVt) The expression is as follows:
6. The method for controlling the electric vehicle charging and battery replacing station considering the battery replacing demand and the photovoltaic uncertainty as claimed in claim 5, wherein the sequence operation is performed on the objective function in the step (2), and an expression is as follows:
wherein, PEL,t(iELt) For a probabilistic sequence of equivalent charging loads in a t-slot charging station, PEL,t(iELt) Has a length of NEL,t，iELtProbabilistic sequence P for equivalent charging load in power change stationEL,t(iELt) The serial number of (a); pb,t(ibt) Unit sequence of charging loads for battery packs of a t-slot converter station, Pb,t(ibt) Length Nb,t，ibtUnit sequence P for charging load of battery pack of battery changing stationb,t(ibt) The serial number of (a); pPV,t(iPVt) Is a time-sequential probabilistic sequence of photovoltaic contribution at time t, PPV,t(iPVt) Has a length of NPV,t，iPVtFor a time-series probabilistic sequence P of photovoltaic outputsPV,t(iPVt) The serial number of (a);
When i isELtWhen P is 0EL,t(0) The sum of the probabilities that all charging loads are less than or equal to the photovoltaic output condition is obtained, namely the negative value part in the difference of the charging loads and the photovoltaic output condition is combined into PEL,t(0) The charging capacity of the power grid is 0, and the actual situation is met;
Let the probabilistic sequence of the electricity purchasing cost of the t-period power change station be Ct(iCt) Sequence ofcolumn length of NCtsequence number is iCtremember pt(ipt) Is a unit sequence of time-of-use electricity price, and the sequence length is Npt，sequence number iptΔ p is the discretization step length of the time-of-use electricity price; then C ist(iCt) From PEL,t(iELt) And pt(ipt) The sequence multiplication calculation obtains: ct(iCt)＝PEL,t(iELt)⊙pt(ipt) (ii) a Calculating a probabilistic sequence C of electricity purchasing cost of the electricity changing station according to the definition of the sequence multiplication operationt(iCt) The expression is:
Wherein iCt＝0,1,…,NCt，NCt＝NEL,t·Npt；
the probabilistic sequence of the total electricity purchasing cost of the power station is F (i)F) Sequence length NFSequence number is iF，F(iF) Probabilistic sequence C of electricity purchase costs from a 24-time-slot power stationt(iCt) Volume and calculation yields:
Wherein iF＝0,1,…,NF，
7. The method for controlling the electric vehicle charging and replacing station considering the replacing demand and the photovoltaic uncertainty as claimed in claim 5, wherein the converting the opportunity constraint into the deterministic form in the step (3) is:
Probabilistic sequence N based on constructed swapping requirementsneed,t(iNt) Dividing the possible power conversion requirement in the t period into NNneed,t+1 states, NNneed,tprobabilistic sequence N for power swap requirementsneed,t(iNt) Length of (2), sequence number iNt＝0,1,…,NNneed,tdirectly calculating the cumulative probability of the power change requirements corresponding to each state of the power change requirements in the t period; let Ns(t) providing the minimum value of the number of the battery replacing sets for the battery replacing station required by meeting the opportunity constraint in the time period t, and then Ns(t) sequence number i of corresponding state in probabilistic sequenceNstsatisfy P (i)Nt≤iNst-1) < alpha and P (i)Nt≤iNst) The alpha is a swapping requirement confidence level set by a swapping station operator; accordingly, N satisfying the chance constraint expression (3) of battery replacement requirement is obtaineds(t), namely, the number of battery packs for providing replacement power according to the replacement power station plan under the corresponding confidence level alpha;
According to a probabilistic sequence F (i) of the constructed electricity change station electricity purchase cost all dayF) Dividing the possible all-day electricity purchasing cost of the t period into NF+1 states, NFProbabilistic sequence F (i) for electricity cost purchase throughout the dayF) Length of (2), sequence number iF＝0,1,…,NFDirectly calculating the cumulative probability of the electricity purchasing cost in all days corresponding to each state; is provided withif the t period is the minimum value taken when the opportunity constraint is satisfied, thensequence number i of corresponding state in probabilistic sequencefSatisfy P (i)F≤if-1) < beta and P (i)F≤if) Beta is more than or equal to beta, and beta is a daily electricity purchasing cost confidence level set by an operator of the electricity changing station; obtaining an opportunity constraint expression (4) satisfying the daily electricity purchasing cost of the power stationnamely toAnd the minimum value of the electricity purchasing cost of the electricity swapping station all day is set by the operator of the electricity swapping station under the confidence level of the electricity purchasing cost per day.
8. The method for controlling the electric vehicle charging and replacing power station considering the battery replacing demand and the photovoltaic uncertainty as claimed in claim 1, wherein the step 3) comprises:
(1) Risk index for battery replacement demand not being met
Firstly, making the following power change rule for the actual operation of the power change station:
Rolling and updating the number of battery packs providing battery replacement in the battery replacement station according to the actual battery replacement requirement, and transferring the remaining full-battery packs in the battery replacement station in the current time period to the battery pack reserve providing battery replacement in the next time period if the battery replacement requirement in the previous time period is lower than the number of battery packs providing battery replacement in the battery replacement station; if the battery replacement demand is higher than the number of battery packs for providing battery replacement by the battery replacement station in the current time period, the part of the battery packs for providing battery replacement by the battery replacement station in the current time period is not replaced,
with Ns' (t) represents the updated number of battery packs for providing replacement power by the replacement power station, and the expression is as follows:
Wherein N isneed(t) is the actual battery replacement demand in the time period t, Ns(t +1) providing the number of battery packs for power conversion for the power conversion station plan in the t +1 time period;
Number D of electric vehicles incapable of changing power in t periodNS(t) the expression is:
since the battery change requirement is a random variable, DNS(t) is also a random variable, taking the expected value E (D)NS(t)) represents the electricity switching demand which is not met in the period of t, quantifies the risk index which is not met by the electricity switching demand, and the punishment cost C corresponding to the risk index which is not met by the electricity switching demandDNSthe expression is as follows:
In the formula, λdnsunit penalties for the power change station failing to meet the power change demand;
Recording a probabilistic sequence corresponding to the number of battery packs for providing battery replacement by the battery replacement station within the updated t-period as N 'according to the established actual operation rule of the battery replacement station'St(i'st) Of sequence length N'NstThe sequence number is i'stRecording a probabilistic sequence corresponding to the number of remaining full-battery packs in the power station during the time period t as Δ Nt'(it) Sequence length NΔNtsequence number itThen Δ Nt'(it) From N'St(i'st) And Nneed,t(iNt) The volume difference calculation yields:
ΔNt'(it)＝N'St(i'st)ΘNneed,t(iNt) (18)
Wherein it＝0,1,…,NΔNt，NΔNt＝N'Nst；Nneed,t(iNt) A probabilistic sequence of power change requirements with sequence number iNt，NNneed,tprobabilistic sequence N for power swap requirementsneed,t(iNt) The length of (a) of (b),
Let NS(t+1)(is(t+1)) A probabilistic sequence corresponding to the number of battery groups for providing power conversion for the original plan of the power conversion station in the time period of t +1, wherein the sequence length is NNs(t+1)Sequence number is(t+1)The probabilistic sequence corresponding to the number of battery packs for providing power swapping by the power swapping station updated in the period of t +1 is N'S(t+1)(i's(t+1)) Of sequence length N'Ns(t+1)The sequence number is i's(t+1)From NS(t+1)(is(t+1)) And Δ Nt'(it) Volume and calculation yields:
Wherein, i's(t+1)＝0,1,…,N'Ns(t+1)，N'Ns(t+1)＝NNs(t+1)+NΔNt；
recording a probabilistic sequence of the battery replacement demand which is not met in the period t as DNSt(iDt) Sequence length NDtSequence number iDtthen, then
DNSt(iDt)＝Nneed,t(iNt)ΘN'St(i'st) (20)
In the formula iDt＝0,1,…,NDt，NDt＝NNneed,tExpected value E (D) of the battery replacement demand that is not satisfied in the period tNS(t)) is:
substituting penalty cost C corresponding to risk indexDNSAn expression (17) is used for solving a punishment cost corresponding to the risk that the battery replacement demand is not met;
(2) Cost underestimation risk indicator
Quantifying the cost underestimation by using the risk index, and setting the penalty cost C of the cost underestimationunderThe expression is as follows:
Cunder＝E(funder)·λunder (22)
Wherein, E (f)under) To cost-underestimate the expected value of the part, λunderUnit penalty for underestimating the control cost of the power station day ahead;
The sequence of the cost-underestimated part is denoted as funder(iF) Sequence number iFThen the expression is:
Wherein: delta P is the discretization step length of photovoltaic output; Δ p is the discretization step of the time of use electricity price,The minimum value of the objective function is taken when the confidence level is not lower than beta, wherein beta is the confidence level of the daily electricity purchasing cost set by the operator of the power station, and the expected value expression of the cost underestimation part is as follows:
penalty cost C for substitution cost underestimationunderAn expression (22) for solving a penalty cost corresponding to the cost underestimation;
(3) penalty cost corresponding to day-ahead control risk
Penalty cost C corresponding to day-ahead control riskriskPenalty cost C underestimated by costunderpunishment cost C corresponding to risk index not meeting battery replacement requirementDNSThe expression is as follows:
Crisk＝CDNS+Cunder (25)。
9. The method for controlling the electric vehicle charging and replacing station considering the battery replacing demand and the photovoltaic uncertainty as recited in claim 1, wherein the determining of the battery pack load margin in the battery replacing station in step 4) is to represent the state of the battery pack by a three-dimensional row vector according to the state parameters of the battery pack in the battery replacing station, and establish the state matrix of the battery pack as follows:
Status＝(n,SocN,Ts) (26)
in the formula, n is the current charging state identifier of the battery pack:
SocN represents the current state of charge; t issIndicating the time when the battery pack is changed, namely the starting time of charging the battery pack;
According to the abovethe battery pack state matrix divides the controlled states of the standby battery pack in the time-slot power conversion station into the following 4 states: the number of battery packs being charged is N1(t) the number of battery packs whose charging must be stopped is N2(t) the number of fully charged battery packs is N3(t) and the number of battery packs to be charged is N4(t); the first three battery packs are all in an uncontrollable state; in order to meet the power change requirement at the subsequent time, a part of battery packs to be charged at each time must be connected to a power grid for charging, the part of battery packs are in an uncontrollable state, and the number of the battery packs is N4-1(t), the rest of the batteries to be charged are in a controllable state, and the number of the batteries to be charged is N4-2(t)；
according to the definition of various states of the battery pack, 4 state vectors corresponding to the battery pack with various states are represented as follows:
Therein, SOCmaxrepresents the battery pack full state charge amount;
the battery pack load margin in the t +1 time period is influenced by the battery pack charging load in the t time period and the battery replacement requirement in the t +1 time period, and battery pack state information in the battery replacement station in the t time period and the t +1 time period is calculated through a battery pack state information calculation expression:
Wherein: n is a radical ofleft(t) represents the number of battery packs remaining to be charged for a period of t; n is a radical ofneed(t +1) is the battery replacement requirement obtained through Monte Carlo simulation; pmin(t)、Pmax(t) the upper and lower limits of the battery pack charging load margin of the battery replacing station after the battery replacing requirement is considered; pb(t) charging load of a battery pack in the power station is changed in a time period t, namely a decision variable controlled by the power station day ahead; pratedAverage charging power for the battery pack;
Considering the uncertainty of the battery replacement requirement, the probability of meeting the actual battery replacement requirement is not lower than the battery replacement stationthe confidence level alpha of the power change requirement set by the operator and the number N of battery sets for power change provided by the power change station plan in the time period of t +1S(t +1) alternative battery state information estimation expression (28) for Nneed(t+1)。
10. The electric vehicle charging and replacing station control method considering the battery replacing demand and the photovoltaic uncertainty as claimed in claim 1, wherein the step 4) of solving the battery replacing station day-ahead control mathematical model through the battery pack load margin based on the genetic algorithm comprises the steps of:
(1) Selecting decision variables of genetic algorithms
Setting decision variables in genetic algorithm to 24 [0,1 ]]Represents the charging load P of the battery pack in the power conversion stationb(t +1) is located in the feasible solution space, then
Pb(t+1)＝Pmin(t+1)+x(t)·(min{Pmax(t+1),PM}-Pmin(t+1)) (29)
wherein, Pmin(t+1)、Pmax(t +1) considering the upper and lower limits of the battery pack charging load margin of the battery replacing station after the battery replacing requirement is considered in the time period of t + 1; pMThe maximum charging power is limited by the number of chargers for the photovoltaic power changing station;
(2) Setting fitness function of genetic algorithm
each individual in the population randomly generated by the genetic algorithm represents a different charging plan, and the power purchase cost probabilistic sequence F (i) corresponding to each charging plan is calculatedF) And obtaining a fitness function of 1/minF by a target function minF which enables the battery pack of the battery changing station to be accessed into the power grid for charging and has the minimum cost;
(3) And substituting the decision variable x (t) and the fitness function 1/minF into a genetic algorithm to obtain the optimal solution of the current switching station day-ahead control mathematical model.
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