CN105720597A - Method for optimizing charge and discharge power of electric car charge-storage-discharge integrated power station - Google Patents

Abstract

The invention provides a method for optimizing charge and discharge power of an electric car charge-storage-discharge integrated power station. The method is characterized in that the interaction power between an integrated power station and a power grid is optimized through a particle algorithm, so that the difference between peak and valley for the power grid load can be greatly reduced; and the capability that the integrated power station participates in the power grid auxiliary service has positive correlation with the capacity of a power station energy storage system, that is, the greater the capacity becomes, the greater the capability that the integrated power station participates in the power grid auxiliary service becomes, and the more significant the effect of peak clipping and valley filling becomes. The method for optimizing charge and discharge power of an electric car charge-storage-discharge integrated power station can also further reduce the cell loss cost for the charge-storage-discharge integrated power station.

Description

The method storing integration power station charge-discharge electric power for optimizing electric automobile to fill

Technical field

The present invention relates to a kind of electric automobile and electrical network interaction technique field, particularly a kind of method optimizing integration power station and electrical network interaction power.

Background technology

Electric automobile is the study hotspot of auto industry, has become the focus of national governments, energy manufacturer and automobile vendor at present, and the EVProject such as the U.S. plans and " ten thousand, the cities " of China plans.It is anticipated that future is along with the continuous progress of battery technology, petering out of petroleum resources, electric automobile will be popularized gradually.Along with following electric automobile is popularized on a large scale, access grid charging on a large scale, due to randomness and the dispersibility of the load that charges, bring very important impact will to grid generation, transmission of electricity, distribution, electricity consumption, scheduling etc..As caused new load peak, increase network loss, the reduction etc. of some node voltage level.Therefore, how by the charging behavior of conservative control electric automobile, reduce the adverse effect that scale charging electric vehicle brings to electrical network, become major issue urgently to be resolved hurrily.

Current electric automobile and the research of electrical network interaction technique, be partly as the distributed energy storage unit in electrical network using electric automobile, participate in the effect of power grid frequency modulation, peak regulation according to dispatch command.Part have studied battery to electrical network centrally connected power supply (Batterytogrid, B2G) pattern.The development & construction that B2G pattern is electric automobile charging station open new approaches, namely build to fill and store integration power station.But, to filling in the correlational study storing integration power station, relevant optimization method often only considers that power station provides the assessment of the assistant service abilities such as peak-frequency regulation for electrical network, have ignored the battery frequent discharge and recharge impact on battery life.It practice, owing to current battery is expensive, it is bigger that battery cost accounts for power station cost proportion, frequent discharge and recharge will certainly increase battery loss, makes shorter battery life, and then impact is filled and stored power station economy operation, increase the operating cost in power station, affect the effect of electrical network assistant service simultaneously.Therefore; the adverse effect brought to electrical network to evade electric automobile scale to be applied; the economy realizing power station is run, it is ensured that the effect of electrical network assistant service, it is necessary to considering that filling of battery loss cost is stored integration power station running optimizatin method and be optimized research.

Summary of the invention

It is an object of the invention to provide a kind of method storing integration power station charge-discharge electric power for optimizing electric automobile to fill, it can not only well stabilize the load fluctuation of electrical network, reducing network load peak-valley difference, also can efficiently reduce electric automobile fills the cost depletions storing power station battery simultaneously.

It is an object of the invention to be realized by such technical scheme, specifically comprise the following steps that

1) object function that integration power station is optimization aim with electrical network peak-valley difference and battery loss cost minimization is determined；

2) constraints of the charge-discharge electric power in integration construct power station, battery capacity and spare capacity；

3) integrating step 1) described in object function and step 2) described in constraints, build mathematical model of optimization, and utilize particle cluster algorithm to seek this mathematical model of optimization, obtain the optimum charge-discharge electric power that each period integration power station is interactive with electrical network.

Further, step 1) described in electrical network peak-valley difference and battery loss cost minimization be optimization aim the computational methods of object function be

P_lx(t)=P_ly(t)+P_lc(t)+P(t)

$\begin{array}{cc}C=\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{r}_{d}P\left(t\right)& P\left(t\right)\≤0\end{array}$

F=min [α (maxP_lx(t)-minP_lx(t))+βC]

In formula, P_lyT daily load that () is regional power grid, P_lx(t) for electrical network through integration station regulate after load, the integrated battery loss cost of C, r_dFor the battery loss rate amounted to by unit discharge power, take 0.4, α, β respectively load curve peak-valley difference and the weight shared in optimization aim of battery loss cost, and alpha+beta=1, the charge-discharge electric power that the integrated station of P (t) is interactive with electrical network, P_lcT () represents the day charging and conversion electric load of electric automobile in region.

Further, step 2) described in constraints be:

2-1) constraints of charge-discharge electric power P (t) that integration power station is interactive with electrical network is:

-P_max≤P(t)≤P_max

In formula, P_maxRepresent the electrical network peak power to the charging of integration station ,-P_maxRepresent the integration station peak power to electrical network electric discharge；

2-2) the battery capacity constraints in integration power station is:

kQ_max≤Q(t)≤Q_max

In formula, Q_maxBeing integrated the maximum stored energy capacitance in power station, k is spare capacity coefficient；

Constraints 2-3) filling spare capacity is:

Q_b=kQ_max

In formula, Q_bFor spare capacity, Q_maxBeing integrated the maximum stored energy capacitance in power station, k is spare capacity coefficient；

2-4) charge-discharge electric power P (t) that integration power station is interactive with electrical network meets below equation with battery capacity Q (t) in integrated power station:

Δ Q=P (t) × t

Q (t+1)=Q (t)+Δ Q-P_lc(t)

In formula, P_lcT () represents the day charging and conversion electric load of electric automobile in region.

Further, spare capacity coefficient k value is 20%.

Further, step 3) described in utilize particle cluster algorithm to seek mathematical model of optimization method particularly includes:

3-1) input known conditions and algorithm basic parameter, initialize position and the speed of particle；Known conditions includes the charging and conversion electric load of power distribution network daily load and electric automobile in region；

3-2) revise particle position according to constraints；

3-3) calculate the fitness of population, i.e. object function, record particle individuality desired positions and colony's desired positions；

3-4) update particle rapidity and position according to the particle rapidity of particle cluster algorithm and location updating formula；

3-5) check whether particle position exceedes limit value, be then return step 3-2), otherwise then proceed to step 3-6)；

3-6) calculate the fitness of population, update and record the most optimal location of particle optimal location and colony；

3-7) judge whether to reach to preset termination condition, be, stop calculating, and export now particle position, charge-discharge electric power P (t) that namely integration power station is interactive with electrical network, otherwise then return step 3-4).

Further, step 3-4) described in particle rapidity and location updating formula particularly as follows:

$v_{\mathrm{Id}}^{k+1}={\mathrm{\ωv}}_{\mathrm{Id}}^k+c_1{\mathrm{rand}}_1^k({\mathrm{pbest}}_{\mathrm{Id}}^k-x_{\mathrm{Id}}^k)+c_2{\mathrm{rand}}_2^k({\mathrm{gbest}}_{\mathrm{Id}}^k-x_{\mathrm{Id}}^k)$

$x_{\mathrm{Id}}^{k+1}=x_{\mathrm{Id}}^k+v_{\mathrm{Id}}^{k+1}$

In formula, v_Id ^kParticle I speed of d dimension in kth time iteration that to be v be；ω is inertia weight；C₁、c₂It is Studying factors, empirically often takes c₁=c₂=2；Rand₁ ^k,rand₂ ^kIt it is the random number between [0,1]；X_Id ^kIt it is particle I position of d dimension in kth time iteration；Pbest_Id ^kIt is that particle I individuality extreme point is at the d coordinate tieed up；Gbest_Id ^kIt is that whole colony global extremum o'clock is at the d coordinate tieed up.

Owing to have employed technique scheme, present invention have the advantage that:

The present invention is by particle algorithm optimization integration power station and electrical network interaction power, the peak-valley difference of network load can be significantly reduced, integration power station participates in the ability of electrical network assistant service and becomes positive correlation with power station energy storage system capacity, namely capacity is more big, the ability that integration power station participates in electrical network assistant service is more strong, the effect of peak load shifting is more notable, the present invention can also reduce further fill store integration power station battery loss cost.

Other advantages of the present invention, target and feature will be illustrated to a certain extent in the following description, and to a certain extent, will be apparent to those skilled in the art based on to investigating hereafter, or can be instructed from the practice of the present invention.The target of the present invention and other advantages can be realized by description below and claims and obtain.

Accompanying drawing explanation

The accompanying drawing of the present invention illustrates as follows.

Fig. 1 is the schematic flow sheet of the present invention；

Fig. 2 is a certain regional distribution network daily load curve figure；

Fig. 3 is the day charging and conversion electric load chart of electric automobile in region；

Fig. 4 is power distribution network daily load curve figure before and after optimizing；

Fig. 5 is station capacity-electrical network peak-valley difference slip curve chart；

Fig. 6 is power station cost depletions figure under different capabilities.

Detailed description of the invention

Below in conjunction with drawings and Examples, the invention will be further described.

A kind of method storing integration power station charge-discharge electric power for optimizing electric automobile to fill, as it is shown in figure 1, comprise the following steps:

1) object function that integration power station is optimization aim with electrical network peak-valley difference and battery loss cost minimization is determined；

2) constraints of the charge-discharge electric power in integration construct power station, battery capacity and spare capacity；

3) integrating step 1) described in object function and step 2) described in constraints, build mathematical model of optimization, and utilize particle cluster algorithm to seek this mathematical model of optimization, obtain the optimum charge-discharge electric power that each period integration power station is interactive with electrical network.

Step 1) described in electrical network peak-valley difference and battery loss cost minimization be optimization aim the computational methods of object function be

P_lx(t)=P_ly(t)+P_lc(t)+P(t)

$\begin{array}{cc}C=\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{r}_{d}P\left(t\right)& P\left(t\right)\≤0\end{array}$

F=min [α (maxP_lx(t)-minP_lx(t))+βC]

In formula, P_lyT daily load that () is regional power grid, P_lx(t) for electrical network through integration station regulate after load, the integrated battery loss cost of C, r_dFor the battery loss rate amounted to by unit discharge power, take 0.4, α, β respectively load curve peak-valley difference and the weight shared in optimization aim of battery loss cost, and alpha+beta=1, the charge-discharge electric power that the integrated station of P (t) is interactive with electrical network, P_lcT () represents the day charging and conversion electric load of electric automobile in region.

Step 2) described in constraints be:

2-1) constraints of charge-discharge electric power P (t) that integration power station is interactive with electrical network is:

-P_max≤P(t)≤P_max

In formula, P_maxRepresent the electrical network peak power to the charging of integration station ,-P_maxRepresent the integration station peak power to electrical network electric discharge；

2-2) the battery capacity constraints in integration power station is:

kQ_max≤Q(t)≤Q_max

In formula, Q_maxBeing integrated the maximum stored energy capacitance in power station, k is spare capacity coefficient；

Constraints 2-3) filling spare capacity is:

Q_b=kQ_max

In formula, Q_bFor spare capacity, Q_maxBeing integrated the maximum stored energy capacitance in power station, k is spare capacity coefficient；

2-4) charge-discharge electric power P (t) that integration power station is interactive with electrical network meets below equation with battery capacity Q (t) in integrated power station:

Δ Q=P (t) × t

Q (t+1)=Q (t)+Δ Q-P_lc(t)

In formula, P_lcT () represents the day charging and conversion electric load of electric automobile in region.

Step 3) described in utilize particle cluster algorithm to seek mathematical model of optimization method particularly includes:

3-1) input known conditions and algorithm basic parameter, initialize position and the speed of particle；Known conditions includes the charging and conversion electric load of power distribution network daily load and electric automobile in region；

3-2) revise particle position according to constraints；

3-3) calculate the fitness of population, i.e. object function, record particle individuality desired positions and colony's desired positions；

3-4) update particle rapidity and position according to the particle rapidity of particle cluster algorithm and location updating formula；

3-5) check whether particle position exceedes limit value, be then return step 3-2), otherwise then proceed to step 3-6)；

3-6) calculate the fitness of population, update and record the most optimal location of particle optimal location and colony；

3-7) judge whether to reach to preset termination condition, be, stop calculating, and export now particle position, charge-discharge electric power P (t) that namely integration power station is interactive with electrical network, otherwise then return step 3-4).

Further, step 3-4) described in particle rapidity and location updating formula particularly as follows:

$v_{\mathrm{Id}}^{k+1}={\mathrm{\ωv}}_{\mathrm{Id}}^k+c_1{\mathrm{rand}}_1^k({\mathrm{pbest}}_{\mathrm{Id}}^k-x_{\mathrm{Id}}^k)+c_2{\mathrm{rand}}_2^k({\mathrm{gbest}}_{\mathrm{Id}}^k-x_{\mathrm{Id}}^k)$

$x_{\mathrm{Id}}^{k+1}=x_{\mathrm{Id}}^k+v_{\mathrm{Id}}^{k+1}$

In formula, v_Id ^kParticle I speed of d dimension in kth time iteration that to be v be；ω is inertia weight；C₁、c₂It is Studying factors, empirically often takes c₁=c₂=2；Rand₁ ^k,rand₂ ^kIt it is the random number between [0,1]；X_Id ^kIt it is particle I position of d dimension in kth time iteration；Pbest_Id ^kIt is that particle I individuality extreme point is at the d coordinate tieed up；Gbest_Id ^kIt is that whole colony global extremum o'clock is at the d coordinate tieed up.

For the daily load of somewhere power distribution network, as shown in Figure 2.Having accessed maximum energy-storage battery capacity in region is that filling of 8MWh stores integration power station, its electric automobile day charging and conversion electric load as shown in Figure 3.

Can obtaining according to Fig. 2 and Fig. 3, the peak load period of the electrical network daily load of this area is 11:00-15:00, and the paddy load period is 04:00-07:00；And the charging and conversion electric peak load period 10:00-14:00 of electric automobile, the paddy load period is 03:00-06:00, and both variation tendencies are substantially uniform.Such variation tendency, can cause that overlap occurs in peak load, increase electrical network peak-valley difference, " Shang Jia peak, peak " phenomenon occur, reduce load rate of grid so that network load fluctuation strengthens further, is unfavorable for power network safety operation.

Before optimizing in region, daily load is that power distribution network daily load superposes with electric automobile day charging and conversion electric load.After Fig. 4 show employing optimization method, with the correlation curve figure optimizing front power distribution network daily load.As shown in Figure 4, regional power grid consider electric automobile charging and conversion electric load after daily load the period about 12:00-14:00, the period of paddy load about 05:00-07:00 of peak load occur.In addition; compared with the optimization method not considering power station; after the optimization method adopting the present invention formulates integration power station discharge and recharge plan; electrical network daily load is significantly improved; reduce electrical network peak load; increase electric-net valley load, can effectively evade the load fluctuation that scale charging electric vehicle brings to electrical network, stabilize electrical network peak-valley difference.

In order to without loss of generality, the integration station that energy-storage battery capacity is also the different capabilities such as 4MWh, 5MWh, 5.5MWh, 6MWh, 7MWh by the present invention respectively has emulated, and further study the change of the integration station energy-storage battery capacity impact on optimisation strategy result.Fig. 5 is the relation of network load peak-valley difference slip and integrated station capacity.

From figure 5 it can be seen that when stored energy capacitance is 4MWh, 5MWh, 5.5MWh, 6MWh, 7MWh, 8MWh, electrical network peak-valley difference slip is respectively as follows: 5.4%, 9.4%, 10.5%, 13.1%, 15.1%.The change positive correlation of integrated station energy storage system capacity is stored in the change of network load peak-valley difference slip with filling, it is gradually increased along with the increase of capacity, when capacity is 4MW, network load peak-valley difference slip is minimum, being 5.4%, when capacity is 8MW, network load peak-valley difference slip is 15.1% to the maximum, during all the other capacity, then fall between.But this positive correlation is not linear relationship, but start to increase very fast, after increase relatively slow, increase when being specially 4MW～6MW than very fast, the amplitude of growth is relatively big, and 6MW～8MW increases relatively slow, and the amplitude of growth is less.It is more big that this may be primarily due to integration power station energy storage system capacity, the ability participating in electrical network adjustment is more strong, and battery discharge capacity increases so that its cost depletions increases, and optimization method is for reducing its cost depletions, the method having to take to trade off, the increasing degree of the peak load shifting ability that slowed down.

Fig. 6 optimization method of the present invention with do not take into account the comparison of energy-storage battery cost depletions and energy-storage battery capacity under the optimization method of battery loss.It can be seen that under optimization method of the present invention, when integration station energy-storage battery capacity is 4MWh, 5MWh, 6MWh, 7MWh, 8MWh, battery loss cost respectively 1053 yuan, 1184 yuan, 1464 yuan, 2012 yuan, 5150 yuan.And under taking no account of battery loss Cost Optimization Approach, battery loss cost respectively 1489 yuan, 1681 yuan, 1976 yuan, 2768 yuan, 7004 yuan.Battery loss cost increases along with integration station energy-storage battery capacity and increases.This is primarily due to along with the increase of integration station energy-storage battery capacity, and required battery necessarily increases, then, when single battery cost depletions is certain, total battery loss cost also can increase.In addition, battery loss cost under optimization method of the present invention is significantly lower than taking no account of battery loss Cost Optimization Approach, adopt the optimization method that the present invention proposes, can while stabilizing load fluctuation, peak load shifting as far as possible, integration station battery cost depletions can also be effectively reduced, significantly improve to fill and store integration station performance driving economy, when popularizing along with electric automobile in future, when filling the quantity storing integration station and capacity substantial increase, reducing battery loss cost will be considerable to the economic benefit that integration station is brought.

From result above it can be seen that utilize a kind of electric automobile of the present invention to fill store the optimal control method that integration power station is run, network load peak-valley difference can be significantly reduced, the assistant services such as peak load shifting are provided for electrical network.Integration power station participates in the ability of electrical network assistant service and becomes positive correlation with power station energy storage system capacity, and namely capacity is more big, and the ability that integration station participates in electrical network assistant service is more strong, and peak load shifting effect is more notable.Compared with the optimization method not taking into account battery loss cost, optimization method of the present invention is under reducing network load peak-valley difference, it is also possible to reduces further and fills the battery loss cost storing integration power station, is a kind of optimal control method economic, reliable.

What finally illustrate is, above example is only in order to illustrate technical scheme and unrestricted, although the present invention being described in detail with reference to preferred embodiment, it will be understood by those within the art that, technical scheme can be modified or equivalent replacement, without deviating from objective and the scope of the technical program, it all should be encompassed in the middle of scope of the presently claimed invention.

Claims (6)

1. the method storing integration power station charge-discharge electric power for optimizing electric automobile to fill, it is characterised in that specifically comprise the following steps that

1) object function that integration power station is optimization aim with electrical network peak-valley difference and battery loss cost minimization is determined；

2) constraints of the charge-discharge electric power in integration construct power station, battery capacity and spare capacity；

3) integrating step 1) described in object function and step 2) described in constraints, build mathematical model of optimization, and utilize particle cluster algorithm to seek this mathematical model of optimization, obtain the optimum charge-discharge electric power that each period integration power station is interactive with electrical network.

2. the as claimed in claim 1 method storing integration power station charge-discharge electric power for optimizing electric automobile to fill, it is characterised in that step 1) described in electrical network peak-valley difference and battery loss cost minimization be optimization aim the computational methods of object function be

P_lx(t)=P_ly(t)+P_lc(t)+P(t)

$C=\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{r}_{d}P\left(t\right),P\left(t\right)\≤0$

F=min [α (maxP_lx(t)-minP_lx(t))+βC]

In formula, P_lyT daily load that () is regional power grid, P_lx(t) for electrical network through integration station regulate after load, the integrated battery loss cost of C, r_dFor the battery loss rate amounted to by unit discharge power, take 0.4, α, β respectively load curve peak-valley difference and the weight shared in optimization aim of battery loss cost, and alpha+beta=1, the charge-discharge electric power that the integrated station of P (t) is interactive with electrical network, P_lcT () represents the day charging and conversion electric load of electric automobile in region.

3. the as claimed in claim 1 method storing integration power station charge-discharge electric power for optimizing electric automobile to fill, it is characterised in that step 2) described in constraints be:

2-1) constraints of charge-discharge electric power P (t) that integration power station is interactive with electrical network is:

-P_max≤P(t)≤P_max

In formula, P_maxRepresent the electrical network peak power to the charging of integration station ,-P_maxRepresent the integration station peak power to electrical network electric discharge；

2-2) the battery capacity constraints in integration power station is:

kQ_max≤Q(t)≤Q_max

In formula, Q_maxBeing integrated the maximum stored energy capacitance in power station, k is spare capacity coefficient；

Constraints 2-3) filling spare capacity is:

Q_b=kQ_max

In formula, Q_bFor spare capacity, Q_maxBeing integrated the maximum stored energy capacitance in power station, k is spare capacity coefficient；

2-4) charge-discharge electric power P (t) that integration power station is interactive with electrical network meets below equation with battery capacity Q (t) in integrated power station:

Δ Q=P (t) × t

Q (t+1)=Q (t)+Δ Q-P_lc(t)

In formula, P_lcT () represents the day charging and conversion electric load of electric automobile in region.

4. the method storing integration power station charge-discharge electric power for optimizing electric automobile to fill as claimed in claim 3, it is characterised in that: spare capacity coefficient k value is 20%.

5. the as claimed in claim 3 method storing integration power station charge-discharge electric power for optimizing electric automobile to fill, it is characterised in that step 3) described in utilize particle cluster algorithm to seek mathematical model of optimization method particularly includes:

3-1) input known conditions and algorithm basic parameter, initialize position and the speed of particle；Known conditions includes the charging and conversion electric load of power distribution network daily load and electric automobile in region；

3-2) revise particle position according to constraints；

3-3) calculate the fitness of population, i.e. object function, record particle individuality desired positions and colony's desired positions；

3-4) update particle rapidity and position according to the particle rapidity of particle cluster algorithm and location updating formula；

3-5) check whether particle position exceedes limit value, be then return step 3-2), otherwise then proceed to step 3-6)；

3-6) calculate the fitness of population, update and record the most optimal location of particle optimal location and colony；

3-7) judge whether to reach to preset termination condition, be, stop calculating, and export now particle position, charge-discharge electric power P (t) that namely integration power station is interactive with electrical network, otherwise then return step 3-4).

6. the as claimed in claim 5 method storing integration power station charge-discharge electric power for optimizing electric automobile to fill, it is characterised in that step 3-4) described in particle rapidity and location updating formula particularly as follows:

$v_{\mathrm{Id}}^{k+1}=\mathrm{\ω}{v}_{\mathrm{Id}}^k+c_1{\mathrm{rand}}_1^k({\mathrm{pbest}}_{\mathrm{Id}}^k-x_{\mathrm{Id}}^k)+c_2{\mathrm{rand}}_2^k({\mathrm{gbest}}_{\mathrm{Id}}^k-x_{\mathrm{Id}}^k)$

$x_{\mathrm{Id}}^{k+1}=x_{\mathrm{Id}}^k+v_{\mathrm{Id}}^{k+1}$

In formula, v_Id ^kParticle I speed of d dimension in kth time iteration that to be v be；ω is inertia weight；C₁、c₂It is Studying factors, empirically often takes c₁=c₂=2；Rand₁ ^k,rand₂ ^kIt it is the random number between [0,1]；X_Id ^kIt it is particle I position of d dimension in kth time iteration；Pbest_Id ^kIt is that particle I individuality extreme point is at the d coordinate tieed up；Gbest_Id ^kIt is that whole colony global extremum o'clock is at the d coordinate tieed up.