CN102436607A - Multi-time-scale decision method for charging power of electric automobile charging station - Google Patents

Abstract

The invention discloses a design of a multi-time-scale decision method for charging power of an electric automobile charging station. The method comprises the following implementation steps of: finishing the current plan by acquiring the current predicted data; finishing a rolling plan by updating the predicted data of the rest time interval; finishing real-time scheduling by acquiring the predicted data in an ultra-short period; and during operation on the same day, performing rolling modification on the charging plan of each electric automobile charging station and performing real-time scheduling on the charging plan of each electric automobile charging station so as to minimize weakening effect of the charging plan of each electric automobile charging station on the actual equivalent load curve smooth effect on the same day caused by the prediction error. The operating plan curve of the energy-storing charging station can be adjusted flexibly and the benefit of the energy-storing resource can be exerted as much as possible according to the changes of the power system load and the operating mode and the change of the generating power of renewable energy sources such as wind power and the like.

Description

The yardstick decision-making technique of many time of electric automobile electrical changing station charge power

Technical field

The present invention relates to a kind of yardstick decision-making technique of many time of electric automobile electrical changing station charge power.This method is through rationally arranging the charging plan of each electric automobile electrical changing station; Can satisfy the electric automobile energy demand of each electric automobile electrical changing station; Can dwindle the peak valley rate of equivalent load curve again; Improve the area power grid part throttle characteristics, thereby reach the peak regulation pressure that reduces conventional unit, improve the purpose that regenerative resource inserts level.The foundation of the yardstick cooperative scheduling rolling optimization decision model of many time through plan a few days ago, rolling planning, Real-Time Scheduling can be subdued the influence of new forms of energy predicated errors such as wind-powered electricity generation, photovoltaic step by step, further improves the part throttle characteristics of equivalent load.

Background technology

The technical development of renewable energy power generation such as wind-power electricity generation, photovoltaic generation mode is ripe gradually, and proportion raises year by year in generating capacity.But wind energy, sun power equal energy source have randomness and intermittent characteristics; Especially it is many that wind-power electricity generation has the generating at night; The anti-peak regulation characteristic that daytime is lower; Make the peak valley rate of the equivalent load curve be made up of load, wind power, photovoltaic generation power in the system than the peak valley rate of original load curve obvious increase arranged, the peak regulation pressure that fired power generating unit faces in the system strengthens, from and restricted the ability that electrical network is admitted more windy power Generate, Generation, Generator volt generated output.

Development, the especially construction of charging electric vehicle infrastructure of intelligent grid technology provide the foundation on the intelligent grid platform, realizing wind-powered electricity generation, photovoltaic generation and the utilization of electric automobile energy storage coordination optimization.A series of charging electric vehicles show the correlative study of electric network influencing: the advantage of electric automobile low emission is only just more remarkable in the zone that with low-carbon (LC) electric power is the master, and not remarkable in the zone that with the coal fired power generation is the master; Having only regenerative resources such as adopting wind-powered electricity generation, photovoltaic generation as much as possible is charging electric vehicle, just can give full play to the reduction of discharging benefit of electric automobile; In addition,, can increase the peak load of electrical network, force the more peaking power source of power grid construction if charging electric vehicle is not guided.

Therefore be necessary in dispatching of power netwoks, charging electric vehicle and regenerative resources such as wind-powered electricity generation, photovoltaic generation are merged each other, raising energy-saving and emission-reduction benefit.Construction and operation that electrical changing station is filled in energy storage are in pilot phase at present, still do not have ripe energy storage Schedule System.Existing technology discharges and recharges operating scheme based on fixing energy storage, and system architecture is simple, but can not weaken the benefit that dispatching of power netwoks is participated in energy storage according to network load and changes of operating modes and regenerative resource variable power adjustment energy storage operation plan scheme.

Summary of the invention

The object of the invention is exactly for addressing the above problem; Develop a kind of yardstick decision-making technique of many time based on the prediction and the electric automobile electrical changing station charge power of dynamic programming; Can be according to renewable energy power generation variable power such as network load and changes of operating modes and wind-powered electricity generations; The operational plan curve of electrical changing station, the benefit of performance energy storage resource as much as possible are filled in the adjustment energy storage flexibly.This system can satisfy the electric automobile energy demand of each electric automobile electrical changing station; Can dwindle the peak valley rate of equivalent load curve again; Improve the area power grid part throttle characteristics, thereby reach the peak regulation pressure that reduces conventional unit, improve the purpose that regenerative resource inserts level.The foundation of the yardstick cooperative scheduling rolling optimization decision model of many time through plan a few days ago, rolling planning, Real-Time Scheduling can be subdued the influence of new forms of energy predicated errors such as wind-powered electricity generation, photovoltaic step by step, further improves the part throttle characteristics of equivalent load.

The objective of the invention is to realize by following technical scheme:

A kind of yardstick decision-making technique of many time of electric automobile electrical changing station charge power, it may further comprise the steps:

A. from the prognoses system at regional power grid scheduling center obtain to next day each load power demand, each wind energy turbine set wind power and each photovoltaic plant output power in the power prediction value of each period; Described each power prediction value all depends in the reality each power measurement apparatus and detects and obtain historical data; Obtain through corresponding prediction algorithm; Thereby accomplishing a few days ago the power prediction data obtains; The power prediction data that utilization is obtained are obtained by what load, wind energy turbine set wind power and photovoltaic plant output power were formed and are not contained charging electric vehicle power at interior equivalent load curve; Obtain maximum, minimum inventories electric weight that each electric automobile electrical changing station allows at the minimax charge power value of each period of next day, electric automobile energy demand, electrical changing station; Foundation is target to comprise charging electric vehicle power in interior equivalent load curve fluctuation minimum; With the charge power of each electrical changing station in each period is decision variable; With the electric automobile energy demand of each electric automobile electrical changing station, the maximum that electrical changing station allows, the planning model a few days ago that the minimum inventories electric weight is constraint, accomplish plan a few days ago;

B. in same day actual motion; Bring in constant renewal in the predicted value that each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power was remained the period on the same day from the prognoses system at regional power grid scheduling center; Upgrade residue period predicted data thereby accomplish; The power prediction data that utilization is obtained are obtained and were remained the period same day of being made up of load, wind energy turbine set wind power and photovoltaic plant output power and do not contain charging electric vehicle power at interior equivalent load curve; Each rolling planning all to remaining the period on same day; Foundation is target to comprise charging electric vehicle power in interior equivalent load curve fluctuation minimum; With the charge power of each electrical changing station in each period is decision variable; With the electric automobile energy demand of each electric automobile electrical changing station, the maximum that electrical changing station allows, the rolling planning model that the minimum inventories electric weight is constraint, accomplish rolling planning;

C. obtain the predicted value of each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power of corresponding period of Real-Time Scheduling from the prognoses system at regional power grid scheduling center, obtain thereby accomplish ultrashort phase predicted data; Foundation is to eliminate the power difference between the prediction of rolling forecast and ultrashort phase; Alleviating the conventional power supply of thermoelectricity is target at the real-time equalized pressure of the power of this period; Charge power with this each electrical changing station of period is a decision variable; With the electric automobile energy demand of each electric automobile electrical changing station, the maximum that electrical changing station allows, the Real-Time Scheduling model that the minimum inventories electric weight is constraint, accomplish Real-Time Scheduling.

Said predicted data is a few days ago obtained with plan a few days ago and is comprised:

1) predicted data is obtained a few days ago

From the prognoses system at regional power grid scheduling center obtain a few days ago to next day each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power in the predicted value of each period; And obtain the equivalent load curve of forming by load, wind energy turbine set wind power and photovoltaic plant output power thus; Obtain maximum, minimum inventories electric weight that each electric automobile charging station allows at the minimax charge power value of each period of next day, electric automobile energy demand, electrical changing station;

2) plan a few days ago

Plan a few days ago is based on a few days ago the prediction to load, wind energy turbine set wind power and photovoltaic plant output power; Arrange the charging plan of each electric automobile electrical changing station next day through rational management; Can either satisfy the electric automobile energy demand of each electric automobile electrical changing station; Dwindle the peak valley rate of equivalent load curve again, thereby reduce the peak regulation pressure of conventional unit, improve the access level of regenerative resource;

● objective function:

$\min \underset{t=1}{\overset{T}{\mathrm{\Σ}}}{(P_{\mathrm{et}}\left(0\right)-P_{\mathrm{av}}\left(0\right))}^2---\left(1\right)$

$P_{\mathrm{et}}\left(0\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(0\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(0\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(0\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(0\right)---\left(2\right)$

$P_{\mathrm{av}}\left(0\right)=\frac{1}{T}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{P}_{\mathrm{et}}\left(0\right)---\left(3\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid; Prediction load d is P at the workload demand of period t a few days ago _{Lt, d}(0); Predict that a few days ago certain wind energy turbine set w is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(0); Predict that a few days ago certain photovoltaic plant s is P in the prediction output power of period t _{St, s}(0); Plan electric automobile electrical changing station e is P at the plan charge power of period t a few days ago _{Ev, t, e}(0); P _Et(0) the equivalent load that load, wind energy turbine set wind power, photovoltaic plant output power and electric automobile electrical changing station charge power form of serving as reasons at period t, P _Av(0) is the mean value of each period equivalent load;

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(0\right)\≤P_{\mathrm{ev},t,e}\left(0\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(0\right)---\left(4\right)$

S _t，ev(0)＝S _t-1，ev(0)+η _cha，ev·P _ev，t，e(0)·Δt-P _ev，t，d(0)·Δt (5)

$S_e^{\min }\left(0\right)\≤S_{t,\mathrm{ev}}\left(0\right)\≤S_e^{\max }\left(0\right)---\left(6\right)$

Allow charge power for the period t that electric automobile electrical changing station e predicted before day is maximum, minimum; P _{Ev, t, d}(0) Δ t is the period t electric automobile energy demand and supply electric weight that electric automobile electrical changing station e predicted before day; S _{T, ev}(0) electric weight that has at the beginning of the end of period t or period t+1 for the electric automobile electrical changing station e of plan a few days ago, wherein S _{0, ev}(0) for electrical changing station e is proxima luce (prox. luc) end dump energy at the beginning of the period 1, but the dump energy actual value of proxima luce (prox. luc) is unknowable when planning a few days ago doing, last remaining electric weight of period in the up-to-date rolling planning that can only think proxima luce (prox. luc) is carried out; η _{Cha, ev}Battery charge efficient for electric automobile electrical changing station e;

Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e.

Said renewal residue period predicted data and rolling planning comprise:

1) upgrades residue period predicted data

Bring in constant renewal in the predicted value that each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power was remained the period on the same day from the prognoses system at regional power grid scheduling center; And obtain the equivalent load curve of forming by load, wind energy turbine set wind power and photovoltaic plant output power that remained the period same day thus; Obtain each electric automobile charging station and remained maximum, the minimum inventories electric weight that the minimax charge power value, electric automobile energy demand, electrical changing station of period allow on the same day;

2) rolling planning

According to existing correlative study; There is certain prediction deviation in forecast a few days ago; Particularly the prediction a few days ago of wind-powered electricity generation and photovoltaic regenerative resource has than large deviation; Therefore the electric automobile electrical changing station charging plan of a few days ago confirming decreases for the smooth effect of load, wind-powered electricity generation and the photovoltaic plant powertrace of actual generation on the same day; The energy demand of electric automobile electrical changing station also had certain variation on the same day in addition, therefore needed the correction of constantly in real time the electric automobile electrical changing station charging plan that remained the period same day being rolled; The enforcement of rolling planning depends on the rolling forecast to load, wind-powered electricity generation, photovoltaic plant power, rolling forecast can utilize continual renovation in real time and measured data, deeply excavate the ruuning situation of following electrical network, for the enforcement of rolling planning provides basic basis; In addition; The charging plan that the enforcement of rolling planning depends on a few days ago to be done; Be that the battery charge power that each rolling planning is confirmed can only be revised in the certain limit of the charge power that plan is a few days ago confirmed; Can consider electrical changing station operating personnel's working strength so indirectly, make rolling planning have the feasibility of reality;

To sum up; Rolling planning is based on the predicted value of load, wind energy turbine set wind power and the photovoltaic plant output power of continuous real-time update on the same day; Remained the charging plan of each electric automobile electrical changing station of period the same day through feasible Real-time and Dynamic correction rationally; Can either satisfy the electric automobile energy demand of each electric automobile electrical changing station; Again the consideration of compromise reducing and the plan a few days ago of trying one's best of the actual charge power of carrying out of actual equivalent load peak-valley difference, taken operating personnel's working strength into account, guarantee that rolling planning has feasibility;

Rolling forecast is once to predict remaining all periods the same day in every m time interval, and rolling planning and rolling forecast mate on time scale each other; Therefore the every m of rolling planning the time interval carries out once;

the inferior rolling planning that is the same day is to remaining period m * (r-1)+1 to period T, T-m * (r-1) the charging plan in the individual time interval is revised altogether;

● objective function:

$\min \underset{t=m(r-1)+1}{\overset{T}{\mathrm{\Σ}}}{(P_{\mathrm{et}}\left(r\right)-P_{\mathrm{av}}\left(r\right))}^2---\left(7\right)$

$P_{\mathrm{et}}\left(r\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(r\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(r\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(r\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(r\right)---\left(8\right)$

$P_{\mathrm{av}}\left(r\right)=\frac{1}{T-m(r-1)}\underset{t=m(r-1)+1}{\overset{T}{\mathrm{\Σ}}}{P}_{\mathrm{et}}\left(r\right)---\left(9\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid; The r time rolling forecast load d is P at the workload demand of period t _{Lt, d}(r); The r time rolling forecast wind energy turbine set w is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(r); The r time rolling forecast photovoltaic plant s is P in the prediction output power of period t _{St, s}(r); The r time rolling planning electrical changing station e is P at the plan charge power of period t _{Ev, t, e}(r); P _Et(r) the equivalent load that load, wind energy turbine set wind power, photovoltaic plant output power and electric automobile electrical changing station charge power form of serving as reasons at period t, P _Av(r) be the mean value of each period equivalent load of remaining the period same day;

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(r\right)\≤P_{\mathrm{ev},t,e}\left(r\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(r\right)---\left(10\right)$

S _t，ev(r)＝S _t-1，ev(r)+η _cha，ev·P _ev，t，e(r)·Δt-P _ev，t，d(r)·Δt (11)

$S_e^{\min }\left(r\right)\≤S_{t,\mathrm{ev}}\left(r\right)\≤S_e^{\max }\left(r\right)---\left(12\right)$

Allow charge power for the period t of the r time rolling forecast of electric automobile electrical changing station e is maximum, minimum; P _{Ev, t, d}(r) Δ t is the period t electric automobile energy demand and supply electric weight of the r time rolling forecast of electric automobile electrical changing station e; S _{T, ev}(r) be the electric weight that the electric automobile electrical changing station e of the r time rolling planning has, wherein S at the beginning of the end of period t or period t+1 _{M (r-1), ev}(r) for electrical changing station e dump energy at the beginning of the end of period m (r-1) is period m (r-1)+1, this value is the charging plan of measured value and period m (r-1) at the beginning of the period m (r-1) and changes electric demand and obtain; η _{Cha, ev}Battery charge efficient for electric automobile electrical changing station e; Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e;

The battery charge power that each rolling planning is confirmed can only be revised in the certain limit of the charge power that plan is a few days ago confirmed; Promptly; The charge power that reduces to carry out with reality of the actual equivalent load peak-valley difference of the consideration of compromising is tried one's best and is planned a few days ago; Taken operating personnel's working strength into account, guaranteed that rolling planning has feasibility;

P _ev，t，e(r)≤P _ev，t，e(0)+ΔP _e(r) (13)

P _ev，t，e(r)≥P _ev，t，e(0)-ΔP _e(r) (14)

Δ P _e(r) modified value for plan in rolling planning, to allow a few days ago.

Said ultrashort phase predicted data is obtained with Real-Time Scheduling and is comprised:

1) ultrashort phase predicted data is obtained

Obtain predicted value from the prognoses system at regional power grid scheduling center to each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power of next scheduling slot; Obtain maximum, minimum inventories electric weight that each electric automobile charging station allows at the minimax charge power value of next scheduling slot, electric automobile energy demand, electrical changing station;

2) Real-Time Scheduling

The main target of Real-Time Scheduling is dynamically to revise rolling planning through the Real-Time Scheduling energy-storage battery, eliminates the power difference between the prediction of rolling forecast and ultrashort phase, alleviates the conventional power supply of thermoelectricity at the real-time equalized pressure of the power of this period; Simultaneously; The battery charge power that Real-Time Scheduling is confirmed can only be revised in the certain limit of the charge power that the rolling planning of up-to-date formulation is confirmed; This is because Real-Time Scheduling does not have the prediction function; Need take into account the objective time interval target of Real-Time Scheduling and remain the electric weight constraint in the period time window same day, therefore need the charge capacity of Real-Time Scheduling objective time interval or power limited in this period charge capacity or power bracket that rolling planning is confirmed;

● objective function:

min(P _et(o)-P _et(r)) ² (15)

$P_{\mathrm{et}}\left(o\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(o\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(o\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(o\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(o\right)---\left(16\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid; Ultrashort phase prediction load d is P at the workload demand of period t _{Lt, d}(o); Ultrashort certain wind energy turbine set w of phase prediction is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(o); Ultrashort certain photovoltaic plant s of phase prediction is P in the prediction output power of period t _{St, s}(o); Electric automobile electrical changing station e is P at the plan charge power of period t in the Real-Time Scheduling _{Ev, t, e}(o);

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(o\right)\≤P_{\mathrm{ev},t,e}\left(o\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(o\right)---\left(17\right)$

S _t，ev(o)＝S _t-1，ev(o)+η _cha，ev·P _ev，t，e(o)·Δt-P _ev，t，d(o)·Δt (18)

$S_e^{\min }\left(o\right)\≤S_{t,\mathrm{ev}}\left(o\right)\≤S_e^{\max }\left(o\right)---\left(19\right)$

Be the maximum charge power that allows of the period t of the ultrashort phase prediction of electric automobile electrical changing station e; S _{T-1, ev}(o) for period t being carried out the measured value of Real-Time Scheduling electrical changing station e storage before electric weight; S _{T-1, cd}(o) for period t being carried out the measured value of Real-Time Scheduling energy storage station cd storage before electric weight;

Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e; P _{Ev, t, d}(o) Δ t is the period t electric automobile energy demand and supply electric weight of the ultrashort phase prediction of electric automobile electrical changing station e;

The battery charge power that Real-Time Scheduling is confirmed can only be revised in the certain limit of the charge power that the rolling planning of up-to-date formulation is confirmed; This is because Real-Time Scheduling does not have the prediction function; Need take into account the objective time interval target of Real-Time Scheduling and remain the electric weight constraint in the period time window same day, so with the charge capacity in Real-Time Scheduling stage or power limited in the scope that rolling planning is confirmed;

P _ev，t，e(o)≤P _ev，t，e(r)+ΔP _e(o) (20)

P _ev，t，e(o)≥P _ev，t，e(r)-ΔP _e(o) (21)

Δ P _e(o) modified value that allows in Real-Time Scheduling for rolling planning.

Through optimizing the electric vehicle charging strategy, to by load, wind-powered electricity generation, photovoltaic, etc. the equivalent load peak valley rate formed, improve part throttle characteristics, it is effective like fired power generating unit peak regulation pressure to alleviate routine.

The beneficial effect of the yardstick decision-making technique of many time of a kind of electric automobile electrical changing station charge power of the present invention's exploitation is embodied in: can be according to renewable energy power generation variable power such as network load and changes of operating modes and wind-powered electricity generations; The operational plan curve of electrical changing station, the benefit of performance energy storage resource as much as possible are filled in the adjustment energy storage flexibly.Arrange the charging plan of each electric automobile electrical changing station through rational management; Can either satisfy the electric automobile energy demand of each electric automobile electrical changing station; Can dwindle the peak valley rate of equivalent load curve again, reduce the peak regulation pressure of conventional unit, improve the access level of regenerative resource.The foundation of the yardstick cooperative scheduling rolling optimization decision model of many time through plan a few days ago, rolling planning, Real-Time Scheduling can be subdued the influence of new forms of energy predicated errors such as wind-powered electricity generation, photovoltaic step by step, further improves the part throttle characteristics of equivalent load.

Description of drawings

The yardstick decision-making technique schematic block diagram of many time of Fig. 1 electric automobile electrical changing station charge power;

The control effect synoptic diagram of Fig. 2 model of the present invention.

Embodiment

Below in conjunction with accompanying drawing and specific embodiment, the yardstick decision-making technique of many time of electric automobile electrical changing station charge power of the present invention is described further:

According to shown in Figure 1, the yardstick decision-making technique of many time of electric automobile electrical changing station charge power of the present invention comprises the steps:

A. a day preceding predicted data is obtained and plan a few days ago:

1) predicted data is obtained a few days ago

From the prognoses system at regional power grid scheduling center obtain a few days ago to next day each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power in the predicted value of each period.And obtain the equivalent load curve of forming by load, wind energy turbine set wind power and photovoltaic plant output power thus, see table 1 for details.Obtain maximum that each electric automobile charging station allows at the minimax charge power value of each period of next day, electric automobile energy demand, electrical changing station, minimum inventories electric weight etc., see table 2 for details.

The equivalent load of forming by load, wind-powered electricity generation, photovoltaic generation of prediction before the table 1 day

The energy demand and the charging ability of table 2 electric automobile electrical changing station

2) plan a few days ago

Plan a few days ago is based on a few days ago the prediction to load, wind energy turbine set wind power and photovoltaic plant output power; Arrange the charging plan of each electric automobile electrical changing station next day through rational management; Can either satisfy the electric automobile energy demand of each electric automobile electrical changing station; Dwindle the peak valley rate of equivalent load curve again, thereby reduce the peak regulation pressure of conventional unit, improve the access level of regenerative resource.

● objective function:

$\min \underset{t=1}{\overset{T}{\mathrm{\Σ}}}{(P_{\mathrm{et}}\left(0\right)-P_{\mathrm{av}}\left(0\right))}^2---\left(1\right)$

$P_{\mathrm{et}}\left(0\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(0\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(0\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(0\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(0\right)---\left(2\right)$

$P_{\mathrm{av}}\left(0\right)=\frac{1}{T}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{P}_{\mathrm{et}}\left(0\right)---\left(3\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid.Prediction load d is P at the workload demand of period t a few days ago _{Lt, d}(0); Predict that a few days ago certain wind energy turbine set w is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(0); Predict that a few days ago certain photovoltaic plant s is P in the prediction output power of period t _{St, s}(0).Plan electric automobile electrical changing station e is P at the plan charge power of period t a few days ago _{Ev, t, e}(0).P _Et(0) the equivalent load that load, wind energy turbine set wind power, photovoltaic plant output power and electric automobile electrical changing station charge power form of serving as reasons at period t, P _Av(0) is the mean value of each period equivalent load.

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(0\right)\≤P_{\mathrm{ev},t,e}\left(0\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(0\right)---\left(4\right)$

S _t，ev(0)＝S _t-1，ev(0)+η _cha，ev·P _ev，t，e(0)·Δt-P _ev，t，d(0)·Δt (5)

$S_e^{\min }\left(0\right)\≤S_{t,\mathrm{ev}}\left(0\right)\≤S_e^{\max }\left(0\right)---\left(6\right)$

Allow charge power for the period t that electric automobile electrical changing station e predicted before day is maximum, minimum; P _{Ev, t, d}(0) Δ t is the period t electric automobile energy demand and supply electric weight that electric automobile electrical changing station e predicted before day; S _{T, ev}(0) electric weight that has at the beginning of the end of period t or period t+1 for the electric automobile electrical changing station e of plan a few days ago, wherein S _{0, ev}(0) for electrical changing station e is proxima luce (prox. luc) end dump energy at the beginning of the period 1, but the dump energy actual value of proxima luce (prox. luc) is unknowable when planning a few days ago doing, last remaining electric weight of period in the up-to-date rolling planning that can only think proxima luce (prox. luc) is carried out; η _{Cha, ev}Battery charge efficient for electric automobile electrical changing station e;

Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e.

The charging planned value of each electric automobile charging station that the plan a few days ago of finding the solution obtains sees table 3 for details.

The charging plan of electric automobile charging station before the table 3 day

B. upgrade residue period predicted data and rolling planning:

1) upgrades residue period predicted data

Bring in constant renewal in the predicted value that each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power was remained the period on the same day from the prognoses system at regional power grid scheduling center.And obtain the equivalent load curve of forming by load, wind energy turbine set wind power and photovoltaic plant output power that remained the period same day thus; The equivalent load curve that remained the period same day of the renewal that per hour obtains sees table 4 for details, and each electric automobile charging station remained maximum that minimax charge power value, electric automobile energy demand, the electrical changing station of period allow, minimum inventories electric weight etc. with predicted data is identical a few days ago on the same day.

The equivalent load of forming by load, wind-powered electricity generation, photovoltaic generation that table 4 roll to be revised

2) rolling planning

According to existing correlative study; There is certain prediction deviation in forecast a few days ago; Particularly the prediction a few days ago of regenerative resource such as wind-powered electricity generation and photovoltaic has than large deviation; Therefore the electric automobile electrical changing station charging plan of a few days ago confirming decreases for the smooth effect of load, wind-powered electricity generation and the photovoltaic plant powertrace of actual generation on the same day; The energy demand of electric automobile electrical changing station also had certain variation on the same day in addition, therefore needed the correction of constantly in real time the electric automobile electrical changing station charging plan that remained the period same day being rolled.The enforcement of rolling planning depends on the rolling forecast to load, wind-powered electricity generation, photovoltaic plant power, rolling forecast can utilize continual renovation in real time and measured data, deeply excavate the ruuning situation of following electrical network, for the enforcement of rolling planning provides basic basis.In addition; The charging plan that the enforcement of rolling planning depends on a few days ago to be done; Be that the battery charge power that each rolling planning is confirmed can only be revised in the certain limit of the charge power that plan is a few days ago confirmed; Can consider electrical changing station operating personnel's working strength so indirectly, make rolling planning have the feasibility of reality.

To sum up; Rolling planning is based on the predicted value of load, wind energy turbine set wind power and the photovoltaic plant output power of continuous real-time update on the same day; Remained the charging plan of each electric automobile electrical changing station of period the same day through feasible Real-time and Dynamic correction rationally; Can either satisfy the electric automobile energy demand of each electric automobile electrical changing station; Again the consideration of compromise reducing and the plan a few days ago of trying one's best of the actual charge power of carrying out of actual equivalent load peak-valley difference, taken operating personnel's working strength into account, guarantee that rolling planning has feasibility.

Rolling forecast is once to predict remaining all periods the same day in every m time interval, and rolling planning and rolling forecast mate on time scale each other.Therefore the every m of rolling planning the time interval carries out once;

the inferior rolling planning that is the same day is to remaining period m * (r-1)+1 to period T, T-m * (r-1) the charging plan in the individual time interval is revised altogether.

● objective function:

$\min \underset{t=m(r-1)+1}{\overset{T}{\mathrm{\Σ}}}{(P_{\mathrm{et}}\left(r\right)-P_{\mathrm{av}}\left(r\right))}^2---\left(7\right)$

$P_{\mathrm{et}}\left(r\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(r\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(r\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(r\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(r\right)---\left(8\right)$

$P_{\mathrm{av}}\left(r\right)=\frac{1}{T-m(r-1)}\underset{t=m(r-1)+1}{\overset{T}{\mathrm{\Σ}}}{P}_{\mathrm{et}}\left(r\right)---\left(9\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid.The r time rolling forecast load d is P at the workload demand of period t _{Lt, d}(r); The r time rolling forecast wind energy turbine set w is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(r); The r time rolling forecast photovoltaic plant s is P in the prediction output power of period t _{St, s}(r); The r time rolling planning electrical changing station e is P at the plan charge power of period t _{Ev, t, e}(r).P _Et(r) the equivalent load that load, wind energy turbine set wind power, photovoltaic plant output power and electric automobile electrical changing station charge power form of serving as reasons at period t, P _Av(r) be the mean value of each period equivalent load of remaining the period same day.

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(r\right)\≤P_{\mathrm{ev},t,e}\left(r\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(r\right)---\left(10\right)$

S _t，ev(r)＝S _t-1，ev(r)+η _cha，ev·P _ev，t，e(r)·Δt-P _ev，t，d(r)·Δt (11)

$S_e^{\min }\left(r\right)\≤S_{t,\mathrm{ev}}\left(r\right)\≤S_e^{\max }\left(r\right)---\left(12\right)$

Allow charge power for the period t of the r time rolling forecast of electric automobile electrical changing station e is maximum, minimum; P _{Ev, t, d}(r) Δ t is the period t electric automobile energy demand and supply electric weight of the r time rolling forecast of electric automobile electrical changing station e; S _{T, ev}(r) be the electric weight that the electric automobile electrical changing station e of the r time rolling planning has, wherein S at the beginning of the end of period t or period t+1 _{M (r-1), ev}(r) for electrical changing station e dump energy at the beginning of the end of period m (r-1) is period m (r-1)+1, this value is the charging plan of measured value and period m (r-1) at the beginning of the period m (r-1) and changes electric demand and obtain; η _{Cha, ev}Battery charge efficient for electric automobile electrical changing station e;

Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e.

The battery charge power that each rolling planning is confirmed can only be revised in the certain limit of the charge power that plan is a few days ago confirmed; Promptly; The charge power that reduces to carry out with reality of the actual equivalent load peak-valley difference of the consideration of compromising is tried one's best and is planned a few days ago; Taken operating personnel's working strength into account, guaranteed that rolling planning has feasibility.

P _ev，t，e(r)≤P _ev，t，e(0)+ΔP _e(r) (13)

P _ev，t，e(r)≥P _ev，t，e(0)-ΔP _e(r) (14)

Δ P _e(r) modified value for plan in rolling planning, to allow a few days ago.

Progressively revise the charging planned value of each electric automobile charging station through rolling planning, the result sees table 5 for details.The actual value of the equivalent load curve that load, wind power, photovoltaic generation power are formed sees table 8 for details.Can know through calculating; Owing to increased the rolling planning link; According to the forecast information of bringing in constant renewal in; Thereby can constantly proofread and correct the charging plan of residue period of each electric automobile charging station, plan link a few days ago and do not have rolling planning link, the equivalent load curve of forming by the charge power of load, wind power, photovoltaic generation power and electric automobile charging station to obtain further smoothly than having only; Can subdue the influence of new forms of energy predicated errors such as wind-powered electricity generation, photovoltaic, further improve the part throttle characteristics of equivalent load.

The charging plan of the electric automobile charging station of table 5 rolling planning correction

C. ultrashort phase predicted data is obtained and Real-Time Scheduling:

1) ultrashort phase predicted data is obtained

Obtain the predicted value of each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power of next scheduling slot is seen for details table 6 from the prognoses system at regional power grid scheduling center, the maximum that each electric automobile charging station allows at minimax charge power value, electric automobile energy demand, the electrical changing station of next scheduling slot, minimum inventories electric weight etc. are with predicted data is identical a few days ago.

The equivalent load of forming by load, wind-powered electricity generation, photovoltaic generation of ultrashort phase of table 6 prediction

2) Real-Time Scheduling

The main target of Real-Time Scheduling is dynamically to revise rolling planning through the Real-Time Scheduling energy-storage battery, eliminates the power difference between the prediction of rolling forecast and ultrashort phase, and conventional power supplys such as alleviation thermoelectricity are at the real-time equalized pressure of the power of this period.Simultaneously; The battery charge power that Real-Time Scheduling is confirmed can only be revised in the certain limit of the charge power that the rolling planning of up-to-date formulation is confirmed; This is because Real-Time Scheduling does not have the prediction function; Need take into account the objective time interval target of Real-Time Scheduling and remain the electric weight constraint in the period time window same day, therefore need the charge capacity of Real-Time Scheduling objective time interval or power limited in this period charge capacity or power bracket that rolling planning is confirmed.

● objective function:

min(P _et(o)-P _et(r)) ² (15)

$P_{\mathrm{et}}\left(o\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(o\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(o\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(o\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(o\right)---\left(16\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid.Ultrashort phase prediction load d is P at the workload demand of period t _{Lt, d}(o); Ultrashort certain wind energy turbine set w of phase prediction is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(o); Ultrashort certain photovoltaic plant s of phase prediction is P in the prediction output power of period t _{St, s}(o); Electric automobile electrical changing station e is P at the plan charge power of period t in the Real-Time Scheduling _{Ev, t, e}(o).

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(o\right)\≤P_{\mathrm{ev},t,e}\left(o\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(o\right)---\left(17\right)$

S _t，ev(o)＝S _t-1，ev(o)+η _cha，ev·P _ev，t，e(o)·Δt-P _ev，t，d(o)·Δt (18)

$S_e^{\min }\left(o\right)\≤S_{t,\mathrm{ev}}\left(o\right)\≤S_e^{\max }\left(o\right)---\left(19\right)$

Be the maximum charge power that allows of the period t of the ultrashort phase prediction of electric automobile electrical changing station e; S _{T-1, ev}(o) for period t being carried out the measured value of Real-Time Scheduling electrical changing station e storage before electric weight; S _{T-1, cd}(o) for period t being carried out the measured value of Real-Time Scheduling energy storage station cd storage before electric weight; Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e; P _{Ev, t, d}(o) Δ t is the period t electric automobile energy demand and supply electric weight of the ultrashort phase prediction of electric automobile electrical changing station e.

The battery charge power that Real-Time Scheduling is confirmed can only be revised in the certain limit of the charge power that the rolling planning of up-to-date formulation is confirmed; This is because Real-Time Scheduling does not have the prediction function; Need take into account the objective time interval target of Real-Time Scheduling and remain the electric weight constraint in the period time window same day, so with the charge capacity in Real-Time Scheduling stage or power limited in the scope that rolling planning is confirmed.

P _ev，t，e(o)≤P _ev，t，e(r)+ΔP _e(o) (20)

P _ev，t，e(o)≥P _ev，t，e(r)-ΔP _e(o) (21)

Δ P _e(o) modified value that allows in Real-Time Scheduling for rolling planning.

The charging planned value of each electric automobile charging station of confirming when further revising rolling planning through Real-Time Scheduling, the result sees table 7 for details.The actual value of the equivalent load curve that load, wind power, photovoltaic generation power are formed sees table 8 for details.Can know through calculating; Owing to increased rolling planning, Real-Time Scheduling link; According to the forecast information of bringing in constant renewal in; Thereby can constantly proofread and correct the charging plan of residue period of each electric automobile charging station, plan link a few days ago and do not have rolling planning link, the equivalent load curve of forming by the charge power of load, wind power, photovoltaic generation power and electric automobile charging station to obtain further smoothly than having only; Can subdue the influence of new forms of energy predicated errors such as wind-powered electricity generation, photovoltaic, further improve the part throttle characteristics of equivalent load.

The charging plan of the electric automobile charging station of table 7 Real-Time Scheduling correction

The actual value of the equivalent load that table 8 is made up of load, wind-powered electricity generation, photovoltaic generation

Fig. 2 is the control effect behind the application model emulation of the present invention.As can beappreciated from fig. 2; Compare with free charge mode (charge value of free charge mode sees table 9 for details); Charging plan has a few days ago produced good smooth effect to load curve; But, the smooth effect of load curve is decreased because the predicated error of regenerative resource such as wind-powered electricity generation is bigger a few days ago.And the yardstick cooperative scheduling decision model of many time through plan a few days ago, rolling planning, Real-Time Scheduling; The working control effect has been compared further improvement with plan a few days ago; Thereby reach the influence of regenerative resource predicated errors such as subduing wind-powered electricity generation, photovoltaic step by step, further improve the part throttle characteristics of equivalent load.

The charge value of the electric automobile charging station of the free charge mode of table 9

The beneficial effect of the yardstick decision-making technique of many time of the electric automobile electrical changing station charge power that the present invention proposes is embodied in: can be according to renewable energy power generation variable power such as network load and changes of operating modes and wind-powered electricity generations; The operational plan curve of electrical changing station, the benefit of performance energy storage resource as much as possible are filled in the adjustment energy storage flexibly.Arrange the charging plan of each electric automobile electrical changing station through rational management; Can either satisfy the electric automobile energy demand of each electric automobile electrical changing station; Can dwindle the peak valley rate of equivalent load curve again, reduce the peak regulation pressure of conventional unit, improve the access level of regenerative resource.The foundation of the yardstick cooperative scheduling rolling optimization decision model of many time through plan a few days ago, rolling planning, Real-Time Scheduling can be subdued the influence of new forms of energy predicated errors such as wind-powered electricity generation, photovoltaic step by step, further improves the part throttle characteristics of equivalent load.

Though the above-mentioned accompanying drawing specific embodiments of the invention that combines is described; But be not restriction to protection domain of the present invention; One of ordinary skill in the art should be understood that; On the basis of technical scheme of the present invention, those skilled in the art need not pay various modifications that creative work can make or distortion still in protection scope of the present invention.

Claims (4)

1. the yardstick decision-making technique of many time of an electric automobile electrical changing station charge power is characterized in that it may further comprise the steps:

A. from the prognoses system at regional power grid scheduling center obtain to next day each load power demand, each wind energy turbine set wind power and each photovoltaic plant output power in the power prediction value of each period; Described each power prediction value all depends in the reality each power measurement apparatus and detects and obtain historical data; Obtain through corresponding prediction algorithm; Thereby accomplishing a few days ago the power prediction data obtains; The power prediction data that utilization is obtained are obtained by what load, wind energy turbine set wind power and photovoltaic plant output power were formed and are not contained charging electric vehicle power at interior equivalent load curve; Obtain maximum, minimum inventories electric weight that each electric automobile electrical changing station allows at the minimax charge power value of each period of next day, electric automobile energy demand, electrical changing station; Foundation is target to comprise charging electric vehicle power in interior equivalent load curve fluctuation minimum; With the charge power of each electrical changing station in each period is decision variable; With the electric automobile energy demand of each electric automobile electrical changing station, the maximum that electrical changing station allows, the planning model a few days ago that the minimum inventories electric weight is constraint, accomplish plan a few days ago;

B. in same day actual motion; Bring in constant renewal in the predicted value that each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power was remained the period on the same day from the prognoses system at regional power grid scheduling center; Upgrade residue period predicted data thereby accomplish; The power prediction data that utilization is obtained are obtained and were remained the period same day of being made up of load, wind energy turbine set wind power and photovoltaic plant output power and do not contain charging electric vehicle power at interior equivalent load curve; Each rolling planning all to remaining the period on same day; Foundation is target to comprise charging electric vehicle power in interior equivalent load curve fluctuation minimum; With the charge power of each electrical changing station in each period is decision variable; With the electric automobile energy demand of each electric automobile electrical changing station, the maximum that electrical changing station allows, the rolling planning model that the minimum inventories electric weight is constraint, accomplish rolling planning;

C. obtain the predicted value of each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power of corresponding period of Real-Time Scheduling from the prognoses system at regional power grid scheduling center, obtain thereby accomplish ultrashort phase predicted data; Foundation is to eliminate the power difference between the prediction of rolling forecast and ultrashort phase; Alleviating the conventional power supply of thermoelectricity is target at the real-time equalized pressure of the power of this period; Charge power with this each electrical changing station of period is a decision variable; With the electric automobile energy demand of each electric automobile electrical changing station, the maximum that electrical changing station allows, the Real-Time Scheduling model that the minimum inventories electric weight is constraint, accomplish Real-Time Scheduling.

2. the yardstick decision-making technique of many time of electric automobile electrical changing station charge power according to claim 1 is characterized in that, said predicted data is a few days ago obtained with plan a few days ago and comprised:

1) predicted data is obtained a few days ago

Saidly promptly refer to next day a few days ago, from the prognoses system at regional power grid scheduling center obtain to next day each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power in the predicted value of each period; And obtain the equivalent load curve of forming by load, wind energy turbine set wind power and photovoltaic plant output power thus; Obtain maximum, minimum inventories electric weight that each electric automobile charging station allows at the minimax charge power value of each period of next day, electric automobile energy demand, electrical changing station;

2) plan a few days ago

Plan a few days ago is based on a few days ago the prediction to load, wind energy turbine set wind power and photovoltaic plant output power; Arrange the charging plan of each electric automobile electrical changing station next day through rational management; Can either satisfy the electric automobile energy demand of each electric automobile electrical changing station; Dwindle the peak valley rate of equivalent load curve again, thereby reduce the peak regulation pressure of conventional unit, improve the access level of regenerative resource;

● objective function:

$\min \underset{t=1}{\overset{T}{\mathrm{\Σ}}}{(P_{\mathrm{et}}\left(0\right)-P_{\mathrm{av}}\left(0\right))}^2---\left(1\right)$

$P_{\mathrm{et}}\left(0\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(0\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(0\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(0\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(0\right)---\left(2\right)$

$P_{\mathrm{av}}\left(0\right)=\frac{1}{T}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{P}_{\mathrm{et}}\left(0\right)---\left(3\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid; Prediction load d is P at the workload demand of period t a few days ago _{Lt, d}(0); Predict that a few days ago certain wind energy turbine set w is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(0); Predict that a few days ago certain photovoltaic plant s is P in the prediction output power of period t _{St, s}(0); Plan electric automobile electrical changing station e is P at the plan charge power of period t a few days ago _{Ev, t, e}(0); P _Et(0) the equivalent load that load, wind energy turbine set wind power, photovoltaic plant output power and electric automobile electrical changing station charge power form of serving as reasons at period t, P _Av(0) is the mean value of each period equivalent load;

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(0\right)\≤P_{\mathrm{ev},t,e}\left(0\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(0\right)---\left(4\right)$

S _t，ev(0)＝S _t-1，ev(0)+η _cha，ev·P _ev，t，e(0)·Δt-P _ev，t，d(0)·Δt (5)

$S_e^{\min }\left(0\right)\≤S_{t,\mathrm{ev}}\left(0\right)\≤S_e^{\max }\left(0\right)---\left(6\right)$

Allow charge power for the period t that electric automobile electrical changing station e predicted before day is maximum, minimum; P _{Ev, t, d}(0) Δ t is the period t electric automobile energy demand and supply electric weight that electric automobile electrical changing station e predicted before day; S _{T, ev}(0) electric weight that has at the beginning of the end of period t or period t+1 for the electric automobile electrical changing station e of plan a few days ago, wherein S _{0, ev}(0) for electrical changing station e is proxima luce (prox. luc) end dump energy at the beginning of the period 1, but the dump energy actual value of proxima luce (prox. luc) is unknowable when planning a few days ago doing, last remaining electric weight of period in the up-to-date rolling planning that can only think proxima luce (prox. luc) is carried out; η _{Cha, ev}Battery charge efficient for electric automobile electrical changing station e;

Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e.

3. the yardstick decision-making technique of many time of electric automobile electrical changing station charge power according to claim 1 is characterized in that, said renewal residue period predicted data and rolling planning comprise:

1) upgrades residue period predicted data

Bring in constant renewal in the predicted value that each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power was remained the period on the same day from the prognoses system at regional power grid scheduling center; And obtain the equivalent load curve of forming by load, wind energy turbine set wind power and photovoltaic plant output power that remained the period same day thus; Obtain each electric automobile charging station and remained maximum, the minimum inventories electric weight that the minimax charge power value, electric automobile energy demand, electrical changing station of period allow on the same day;

2) rolling planning

According to existing correlative study; There is certain prediction deviation in forecast a few days ago; Particularly the prediction a few days ago of wind-powered electricity generation and photovoltaic regenerative resource has than large deviation; Therefore the electric automobile electrical changing station charging plan of a few days ago confirming decreases for the smooth effect of load, wind-powered electricity generation and the photovoltaic plant powertrace of actual generation on the same day; The energy demand of electric automobile electrical changing station also had certain variation on the same day in addition, therefore needed the correction of constantly in real time the electric automobile electrical changing station charging plan that remained the period same day being rolled; The enforcement of rolling planning depends on the rolling forecast to load, wind-powered electricity generation, photovoltaic plant power, rolling forecast can utilize continual renovation in real time and measured data, deeply excavate the ruuning situation of following electrical network, for the enforcement of rolling planning provides basic basis; In addition; The charging plan that the enforcement of rolling planning depends on a few days ago to be done; Be that the battery charge power that each rolling planning is confirmed can only be revised in the certain limit of the charge power that plan is a few days ago confirmed; Can consider electrical changing station operating personnel's working strength so indirectly, make rolling planning have the feasibility of reality;

Rolling forecast is once to predict remaining all periods the same day in every m time interval, and rolling planning and rolling forecast mate on time scale each other; Therefore the every m of rolling planning the time interval carries out once;

the inferior rolling planning that is the same day is to remaining period m * (r-1)+1 to period T, T-m * (r-1) the charging plan in the individual time interval is revised altogether;

● objective function:

$\min \underset{t=m(r-1)+1}{\overset{T}{\mathrm{\Σ}}}{(P_{\mathrm{et}}\left(r\right)-P_{\mathrm{av}}\left(r\right))}^2---\left(7\right)$

$P_{\mathrm{et}}\left(r\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(r\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(r\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(r\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(r\right)---\left(8\right)$

$P_{\mathrm{av}}\left(r\right)=\frac{1}{T-m(r-1)}\underset{t=m(r-1)+1}{\overset{T}{\mathrm{\Σ}}}{P}_{\mathrm{et}}\left(r\right)---\left(9\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid; The r time rolling forecast load d is P at the workload demand of period t _{Lt, d}(r); The r time rolling forecast wind energy turbine set w is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(r); The r time rolling forecast photovoltaic plant s is P in the prediction output power of period t _{St, s}(r); The r time rolling planning electrical changing station e is P at the plan charge power of period t _{Ev, t, e}(r); P _Et(r) the equivalent load that load, wind energy turbine set wind power, photovoltaic plant output power and electric automobile electrical changing station charge power form of serving as reasons at period t, P _Av(r) be the mean value of each period equivalent load of remaining the period same day;

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(r\right)\≤P_{\mathrm{ev},t,e}\left(r\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(r\right)---\left(10\right)$

S _t，ev(r)＝S _t-1，ev(r)+η _cha，ev·P _ev，t，e(r)·Δt-P _ev，t，d(r)·Δt (11)

$S_e^{\min }\left(r\right)\≤S_{t,\mathrm{ev}}\left(r\right)\≤S_e^{\max }\left(r\right)---\left(12\right)$

Allow charge power for the period t of the r time rolling forecast of electric automobile electrical changing station e is maximum, minimum; P _{Ev, t, d}(r) Δ t is the period t electric automobile energy demand and supply electric weight of the r time rolling forecast of electric automobile electrical changing station e; S _{T, ev}(r) be the electric weight that the electric automobile electrical changing station e of the r time rolling planning has, wherein S at the beginning of the end of period t or period t+1 _{M (r-1), ev}(r) for electrical changing station e dump energy at the beginning of the end of period m (r-1) is period m (r-1)+1, this value is the charging plan of measured value and period m (r-1) at the beginning of the period m (r-1) and changes electric demand and obtain; η _{Cha, ev}Battery charge efficient for electric automobile electrical changing station e;

Maximum, minimum storehouse for each period permission of electric automobile electrical changing station e

Deposit electric weight;

The battery charge power that each rolling planning is confirmed can only be revised in the certain limit of the charge power that plan is a few days ago confirmed; Promptly; The charge power that reduces to carry out with reality of the actual equivalent load peak-valley difference of the consideration of compromising is tried one's best and is planned a few days ago; Taken operating personnel's working strength into account, guaranteed that rolling planning has feasibility;

P _ev，t，e(r)≤P _ev，t，e(0)+ΔP _e(r) (13)

P _ev，t，e(r)≥P _ev，t，e(0)-ΔP _e(r) (14)

Δ P _e(r) modified value for plan in rolling planning, to allow a few days ago.

4. the yardstick decision-making technique of many time of electric automobile electrical changing station charge power according to claim 1 is characterized in that said ultrashort phase predicted data is obtained with Real-Time Scheduling and comprised:

1) ultrashort phase predicted data is obtained

Obtain predicted value from the prognoses system at regional power grid scheduling center to each workload demand, each wind energy turbine set wind power and each photovoltaic plant output power of next scheduling slot; Obtain maximum, minimum inventories electric weight that each electric automobile charging station allows at the minimax charge power value of next scheduling slot, electric automobile energy demand, electrical changing station;

2) Real-Time Scheduling

The main target of Real-Time Scheduling is dynamically to revise rolling planning through the Real-Time Scheduling energy-storage battery, eliminates the power difference between the prediction of rolling forecast and ultrashort phase, alleviates the conventional power supply of thermoelectricity at the real-time equalized pressure of the power of this period; Simultaneously; The battery charge power that Real-Time Scheduling is confirmed can only be revised in the certain limit of the charge power that the rolling planning of up-to-date formulation is confirmed; This is because Real-Time Scheduling does not have the prediction function; Need take into account the objective time interval target of Real-Time Scheduling and remain the electric weight constraint in the period time window same day, therefore need the charge capacity of Real-Time Scheduling objective time interval or power limited in this period charge capacity or power bracket that rolling planning is confirmed;

● objective function:

min(P _et(o)-P _et(r)) ² (15)

$P_{\mathrm{et}}\left(o\right)=\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(o\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(o\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(o\right)+\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(o\right)---\left(16\right)$

D, W, S and E are respectively load, wind energy turbine set, photovoltaic plant, the electric automobile electrical changing station number of area power grid; Ultrashort phase prediction load d is P at the workload demand of period t _{Lt, d}(o); Ultrashort certain wind energy turbine set w of phase prediction is P in the prediction wind-powered electricity generation output power of period t _{Wt, w}(o); Ultrashort certain photovoltaic plant s of phase prediction is P in the prediction output power of period t _{St, s}(o); Electric automobile electrical changing station e is P at the plan charge power of period t in the Real-Time Scheduling _{Ev, t, w}(o);

● constraint condition:

$P_{\mathrm{ev},t,e}^{\min }\left(o\right)\≤P_{\mathrm{ev},t,e}\left(o\right)\≤P_{\mathrm{ev},t,e}^{\max }\left(o\right)---\left(17\right)$

S _t，ev(o)＝S _t-1，ev(o)+η _cha，ev·P _ev，t，e(o)·Δt-P _ev，t，d(o)·Δt (18)

$S_e^{\min }\left(o\right)\≤S_{t,\mathrm{ev}}\left(o\right)\≤S_e^{\max }\left(o\right)---\left(19\right)$

Be the maximum charge power that allows of the period t of the ultrashort phase prediction of electric automobile electrical changing station e; S _{T-1, ev}(o) for period t being carried out the measured value of Real-Time Scheduling electrical changing station e storage before electric weight; S _{T-1, cd}(o) for period t being carried out the measured value of Real-Time Scheduling energy storage station cd storage before electric weight;

Maximum, minimum inventories electric weight for each period permission of electric automobile electrical changing station e; P _{Ev, t, d}(o) Δ t is the period t electric automobile energy demand and supply electric weight of the ultrashort phase prediction of electric automobile electrical changing station e;

The battery charge power that Real-Time Scheduling is confirmed can only be revised in the certain limit of the charge power that the rolling planning of up-to-date formulation is confirmed; This is because Real-Time Scheduling does not have the prediction function; Need take into account the objective time interval target of Real-Time Scheduling and remain the electric weight constraint in the period time window same day, so with the charge capacity in Real-Time Scheduling stage or power limited in the scope that rolling planning is confirmed;

P _ev，t，e(o)≤P _ev，t，e(r)+ΔP _e(o) (20)

P _ev，t，e(o)≥P _ev，t，e(r)-ΔP _e(o) (21)

Δ P _e(o) modified value that allows in Real-Time Scheduling for rolling planning.

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