CN102510123A - Automatic energy storage control method for large-scale wind power cutter - Google Patents

Abstract

The invention discloses an automatic energy storage control method for large-scale wind power cutter. The method comprises the following steps: acquiring the measured value of each load demand power value, each wind power station power value and each photovoltaic power station output power value in the current period of time from a SCADA system and a predict system of a regional power grid dispatching center, and ultra-short term predicted value in the period of time and other data; and further acquiring the deviation between the measured value of a power grid load, a wind power and a photovoltaic power station output power value and the predicted value of the power grid load, the wind power and the photovoltaic power station output power value in the ultra-short term prediction. After monitoring large-scale abnormal cutter of a wind power generator unit caused by failure of the power grid or a wind power station, the method provided by the invention can automatically dispatch a charge/discharge device in an energy storing charge and conversion power station, balances the shortage of active power, accelerates the recovery of frequency, improves the stability of the frequency of a system, greatly supports the recovery of power grid failure frequency, and has a broad development prospect.

Description

The reply wind-powered electricity generation is the automatic energy storage control method of cutter on a large scale

Technical field

The present invention relates to a kind of automatic energy storage control method, a kind of specifically wind-powered electricity generation automatic energy storage control method of cutter on a large scale of tackling.

Background technology

The technical development of renewable energy power generation such as wind power generation, photovoltaic generation mode is day by day ripe, and proportion raises year by year in generate output.But wind energy, solar energy equal energy source have randomness and intermittent characteristics, and large-scale wind power cutter fault also happens occasionally, and bring challenges for electric network active balance and frequency stabilization.

Construction and operation that electrical changing station is filled in the electric automobile energy storage are in pilot phase at present, still do not have ripe energy storage Dispatching Control System in the electrical network aspect.Existing technology discharges and recharges operating scheme based on fixing energy storage; System configuration is simple; But can not make response according to emergencies such as network load, operational mode, regenerative resource variable power and electric network faults; Can't give full play to the fast advantage of energy storage charging/discharging apparatus response speed, be unfavorable for that mains frequency is stable, weaken the benefit that energy-storage system is participated in dispatching of power netwoks.

Summary of the invention

The object of the invention is exactly for addressing the above problem, and a kind of reply wind-powered electricity generation based on the automation system for the power network dispatching platform automatic energy storage control method of cutter on a large scale is provided.The charging/discharging apparatus that electrical changing station is filled in energy storage can be dispatched automatically monitoring the wind-powered electricity generation unit on a large scale during the cutter fault by system in this invention.Be transformed into the total power discharge mode by charging modes, effectively remedy the electric network active power shortage, quicken frequency retrieval, improve the system frequency stability characteristic (quality).

The present invention realizes through following technical scheme: the reply wind-powered electricity generation is the automatic energy storage control method of cutter on a large scale, and it may further comprise the steps:

(1), the SCADA system from the regional power grid scheduling center obtains required data;

The predicted value of the data of (2), obtaining the SCADA system at regional power grid scheduling center and the prognoses system of grid dispatching center compares calculates power deviation;

(3), the data obtained according to the SCADA system of regional grid dispatching center and the above-mentioned power deviation parameter of trying to achieve in asking for, utilize Integer programming to find the solution, the electric automobile electrical changing station that can draw correction discharges and recharges performance number;

(4), discharge and recharge performance number, carry out the plan that discharges and recharges according to the electric automobile electrical changing station of above-mentioned correction.

Operation principle and process: data are obtained and power deviation is asked for:

Data are obtained

Obtain the measured value of each workload demand performance number, each wind energy turbine set wind performance number and each photovoltaic plant output power value of current period from the SCADA system at regional power grid scheduling center.The predicted value of each workload demand performance number, each wind energy turbine set wind performance number and each photovoltaic plant output power value that obtains when before the prognoses system at regional power grid scheduling center obtains, this period being carried out ultrashort phase prediction.Obtaining each electric automobile charging station allows charge power value and maximum maximum, minimum inventories electric weight and this period initial time electrical changing station that allows discharge power value, electric automobile energy demand, electrical changing station to allow to store the measured value etc. of electric weight in the maximum of this period.

Power deviation is asked for

According to the data that preceding step obtains, the deviation the when measured value that can ask for network load, wind power and photovoltaic plant output power value was predicted with the ultrashort phase between the predicted value of network load, wind power and photovoltaic plant output power value.

$\mathrm{\ΔP}=\left(\right(\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(o\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(o\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(o\right))-(\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(c\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(c\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(c\right))---\left(1\right)$

Deviation when Δ P is ultrashort phase prediction between the measured value of the predicted value of network load, wind power and photovoltaic plant output power value and network load, wind power and photovoltaic plant output power value; D, W and S are respectively load, wind energy turbine set, the photovoltaic plant 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 power output of period t _{Wt, w}(o); Ultrashort certain photovoltaic plant s of phase prediction is P in the prediction power output of period t _{St, s}(o); Load d is P at the workload demand measured value of period t _{Lt, d}(c); Wind energy turbine set w is P at the wind-powered electricity generation power output measured value of period t _{Wt, w}(c); Photovoltaic plant s is P at the power output measured value of period t _{St, s}(c).

The adjustment strategy of automatic energy-storage system:

1) target function

$\min {(\mathrm{\ΔP}+(\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(o\right)-\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{d,t,e}\left(o\right))-(\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(c\right)-\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{d,t,e}\left(c\right)))}^2---\left(2\right)$

Through target function, the numerical value of the function of acquisition is more little good more.

E is the electric automobile electrical changing station number of area power grid.The electric automobile electrical changing station e that before sets in the operation plan is P at plan charge power and the discharge power of this period _{Ev, t, e}(o) and P _{D, t, e}(o), be known quantity; Charging and discharge power value that electric automobile electrical changing station e revised in this period are P _{Ev, t, e}(c) and P _{D, t, e}(c).

2) constraints

$0\≤P_{\mathrm{ev},t,e}\left(c\right)\≤X_{1,e}\·P_{\mathrm{ev},t,e}^{\max }\left(c\right)---\left(3\right)$

$0\≤P_{d,t,e}\left(c\right)\≤X_{2,e}\·P_{d,t,e}^{\max }\left(c\right)---\left(4\right)$

P _ev，t，e(c)·X _3，e＝0 (5)

P _d，t，e(c)·X _3，e＝0 (6)

X _1，e+X _2，e+X _3，e＝1 (7)

$S_{t,\mathrm{ev}}\left(c\right)=S_{t-1,\mathrm{ev}}\left(c\right)+{\mathrm{\η}}_{\mathrm{cha},\mathrm{ev}}\·P_{\mathrm{ev},t,e}\left(c\right)\·\mathrm{\Δt}-\frac{\mathrm{\Δt}}{{\mathrm{\η}}_{\mathrm{dch},\mathrm{ev}}}\·P_{d,t,e}\left(c\right)-P_{\mathrm{ev},t,d}\left(c\right)\·\mathrm{\Δt}---\left(8\right)$

$S_e^{\min }\left(c\right)\≤S_{t,\mathrm{ev}}\left(c\right)\≤S_e^{\max }\left(c\right)---\left(9\right)$

The present invention with the running status of electric automobile electrical changing station e be divided into electrical network to charging batteries of electric automobile, with batteries of electric automobile in storing electricity to electrical network discharge and idle three kinds of states; X _{1, e}Represented that electric automobile electrical changing station e obtained electric energy from electrical network, X at=1 o'clock _{1, e}Represented that electric automobile electrical changing station e did not obtain electric energy from electrical network at=0 o'clock; X _{2, e}Represented that electric automobile electrical changing station e discharged X to electrical network at=1 o'clock _{2, e}Represented that electric automobile electrical changing station e did not discharge to electrical network at=0 o'clock; X _{3, e}Representing in=1 o'clock does not have energy exchange between electric automobile electrical changing station e and the electrical network, i.e. idle condition, X _{3, e}Represented in=0 o'clock to have energy exchange between electric automobile electrical changing station e and the electrical network, promptly be in busy state; Be respectively the maximum of electric automobile electrical changing station e and allow charge power and discharge power in this period; η _{Cha, ev}, η _{Dch, ev}Be respectively battery charge efficient and the discharging efficiency of electric automobile electrical changing station e; S _{T-1, ev}(c) be the measured value that this period initial time electrical changing station e stores electric weight; S _{T, ev}(c) be the storage electric weight of this moment period Mo electrical changing station e;

Be maximum, the minimum inventories electric weight of electric automobile electrical changing station e in this period permission; Δ t is the duration of this period; P _{Ev, t, d}(c) Δ t is the electric automobile energy demand and supply electric weight of electric automobile electrical changing station e in this period.

This system is based on the power network schedule automation platform; Can monitor wind-powered electricity generation unit operation situation in real time; And the deviation between predicting according to actual measurement wind power, network load and ultrashort phase, the energy storage scheduling scheme is revised, dispatch the charging/discharging apparatus that energy storage fills electrical changing station automatically and respond.

Beneficial effect of the present invention: this system causes monitoring electrical network or wind energy turbine set fault under the situation of the unusual on a large scale cutter of wind turbine generator; Can dispatch the charging/discharging apparatus that electrical changing station is filled in energy storage automatically; Realize that according to emergency control policy energy storage fills the quick switching of electrical changing station charge/discharge mode, on the spot active power vacancy is carried out balance, quicken frequency retrieval; Improve system frequency stability, for the electric network fault frequency retrieval provides support.

Description of drawings

Fig. 1 is for tackling the wind-powered electricity generation automatic energy storage control system block diagram of cutter on a large scale.

Embodiment

Below in conjunction with accompanying drawing and specific embodiment, to the present invention tackle wind-powered electricity generation on a large scale the automatic energy storage control system of cutter be described further:

This system comprises 5 load point, 3 wind energy turbine set, 2 photovoltaic plants, 2 electric automobile electrical changing stations; Be respectively load 1, load 2, load 3, load 4, load 5; Wind energy turbine set 1, wind energy turbine set 2, wind energy turbine set 3, photovoltaic plant 1, photovoltaic plant 2, electrical changing station 1, electrical changing station 2.

According to shown in Figure 1, the present invention tackle wind-powered electricity generation on a large scale the automatic energy storage control system of cutter comprise the steps:

Data are obtained and power deviation is asked for:

1) data are obtained

Obtain the measured value P of each workload demand performance number of current period from the SCADA system at regional power grid scheduling center _{Lt, d}(c), the measured value P of each wind energy turbine set wind performance number _{Wt, w}(c) and the measured value P of each photovoltaic plant output power value _{St, s}The predicted value P of each workload demand performance number that obtains when (c), before the prognoses system at regional power grid scheduling center obtains, this period being carried out ultrashort phase prediction _{Lt, d}(o), the predicted value P of each wind energy turbine set wind performance number _{Wt, w}(o) and the predicted value P of each photovoltaic plant output power value _{St, s}(o), see table 1 for details.Obtain the maximum charge performance number of each electric automobile charging station in this period

Maximum discharge power value

Electric automobile energy demand P _{Ev, t, d}(c) maximum stock's electric weight of Δ t, electrical changing station permission

The minimum inventories electric weight

And this period initial time electrical changing station stores the measured value S of electric weight _{T-1, ev}(c) etc., see table 2 for details.

Table 1 load, wind-powered electricity generation, photovoltaic generation power prediction and measured value

Charging and discharging capabilities, energy demand and the plan of table 2 electric automobile electrical changing station discharge and recharge power

Power deviation is asked for:

According to the data that preceding step obtains, the deviation the when measured value that can ask for network load, wind power and photovoltaic plant output power value was predicted with the ultrashort phase between the predicted value of network load, wind power and photovoltaic plant output power value.

$\mathrm{\ΔP}=(\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(o\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(o\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(o\right))-$ (1)

$(\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(c\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(c\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(c\right))=126.5\mathrm{MW}$

Deviation when Δ P is ultrashort phase prediction between the measured value of the predicted value of network load, wind power and photovoltaic plant output power value and network load, wind power and photovoltaic plant output power value; D, W and S are respectively load, wind energy turbine set, the photovoltaic plant 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 power output of period t _{Wt, w}(o); Ultrashort certain photovoltaic plant s of phase prediction is P in the prediction power output of period t _{St, s}(o); Load d is P at the workload demand measured value of period t _{Lt, d}(c); Wind energy turbine set w is P at the wind-powered electricity generation power output measured value of period t _{Wt, w}(c); Photovoltaic plant s is P at the power output measured value of period t _{St, s}(c).

The adjustment strategy of automatic energy-storage system:

1) target function

$\min {(\mathrm{\ΔP}+(\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(o\right)-\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{d,t,e}\left(o\right))-(\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(c\right)-\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{d,t,e}\left(c\right)))}^2---\left(2\right)$

E is the electric automobile electrical changing station number of area power grid.The electric automobile electrical changing station e that before sets in the operation plan is P at plan charge power and the discharge power of this period _{Ev, t, e}(o) and P _{D, t, e}(o), be known quantity; Charging and discharge power value that electric automobile electrical changing station e revised in this period are P _{Ev, t, e}(c) and P _{D, t, e}(c).

2) constraints

$0\≤P_{\mathrm{ev},t,e}\left(c\right)\≤X_{1,e}\·P_{\mathrm{ev},t,e}^{\max }\left(c\right)---\left(3\right)$

$0\≤P_{d,t,e}\left(c\right)\≤X_{2,e}\·P_{d,t,e}^{\max }\left(c\right)---\left(4\right)$

P _ev，t，e(c)·X _3，e＝0?(5)

P _d，t，e(c)·X _3，e＝0 (6)

X _1，e+X _2，e+X _3，e＝1 (7)

$S_{t,\mathrm{ev}}\left(c\right)=S_{t-1,\mathrm{ev}}\left(c\right)+{\mathrm{\η}}_{\mathrm{cha},\mathrm{ev}}\·P_{\mathrm{ev},t,e}\left(c\right)\·\mathrm{\Δt}-\frac{\mathrm{\Δt}}{{\mathrm{\η}}_{\mathrm{dch},\mathrm{ev}}}\·P_{d,t,e}\left(c\right)-P_{\mathrm{ev},t,d}\left(c\right)\·\mathrm{\Δt}---\left(8\right)$

$S_e^{\min }\left(c\right)\≤S_{t,\mathrm{ev}}\left(c\right)\≤S_e^{\max }\left(c\right)---\left(9\right)$

The present invention with the running status of electric automobile electrical changing station e be divided into electrical network to charging batteries of electric automobile, with batteries of electric automobile in storing electricity to electrical network discharge and idle three kinds of states; X _{1, e}Represented that electric automobile electrical changing station e obtained electric energy from electrical network, X at=1 o'clock _{1, e}Represented that electric automobile electrical changing station e did not obtain electric energy from electrical network at=0 o'clock; X _{2, e}Represented that electric automobile electrical changing station e discharged X to electrical network at=1 o'clock _{2, e}Represented that electric automobile electrical changing station e did not discharge to electrical network at=0 o'clock; X _{3, e}Representing in=1 o'clock does not have energy exchange between electric automobile electrical changing station e and the electrical network, i.e. idle condition, X _{3, e}Represented in=0 o'clock to have energy exchange between electric automobile electrical changing station e and the electrical network, promptly be in busy state;

Be respectively the maximum of electric automobile electrical changing station e and allow charge power and discharge power in this period; η _{Cha, ev}, η _{Dch, ev}Be respectively battery charge efficient and the discharging efficiency of electric automobile electrical changing station e; S _{T-1, ev}(c) be the measured value that this period initial time electrical changing station e stores electric weight; S _{T, ev}(c) be the storage electric weight of this moment period Mo electrical changing station e;

Be maximum, the minimum inventories electric weight of electric automobile electrical changing station e in this period permission; Δ t is the duration of this period; P _{Ev, t, d}(c) Δ t is the electric automobile energy demand and supply electric weight of electric automobile electrical changing station e in this period.

According to the parameter that data are obtained and power deviation is tried to achieve in asking for, utilize Integer programming to find the solution the Mathematical Modeling of forming to formula (9) by formula (2), the electric automobile electrical changing station that can draw correction discharges and recharges performance number, sees table 3 for details.

The electric automobile electrical changing station that table 3 is revised discharges and recharges power

Electric automobile electrical changing station 1 and 2 is given charging batteries of electric automobile in this period in the original plan; Charge power is respectively 15.4MW and 21.6MW; But because the error that the prediction of ultrashort phase exists, make the power deviation that has 126.5MW between measured value and the predicted value, realize that through the adjustment strategy of above-mentioned automatic energy-storage system energy storage fills the quick switching of electrical changing station charge/discharge mode; Become electric automobile electrical changing station 1 and 2 at this period discharge respectively 30.8MW and 42.6MW, make power deviation be reduced into:

(126.5MW-(30.8MW+42.6MW)+(15.4MW+21.6MW))=16.1MW, thus the pressure of conventional unit tracing system power-balance can obviously be weakened, improve the frequency of system.

The reply wind-powered electricity generation that the present invention the proposes advantage applies of the automatic energy storage control system of cutter on a large scale exists: this system is based on the power network schedule automation platform; Can monitor wind-powered electricity generation unit operation situation in real time; And according to the deviation between actual measurement wind power, network load and the prediction of ultrashort phase; The energy storage scheduling scheme is revised, dispatched the charging/discharging apparatus that energy storage fills electrical changing station automatically and respond.This system causes monitoring electrical network or wind energy turbine set fault under the situation of the unusual on a large scale cutter of wind turbine generator; Can dispatch the charging/discharging apparatus that electrical changing station is filled in energy storage automatically; Realize that according to emergency control policy energy storage fills the quick switching of electrical changing station charge/discharge mode, on the spot active power vacancy is carried out balance, quicken frequency retrieval; Improve system frequency stability, for the electric network fault frequency retrieval provides support.

Though the above-mentioned accompanying drawing specific embodiments of the invention that combines is described; But be not restriction to protection range of the present invention; 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. tackle the wind-powered electricity generation automatic energy storage control method of cutter on a large scale for one kind, may further comprise the steps:

(1), the SCADA system from the regional power grid scheduling center obtains required data;

The predicted value of the data of (2), obtaining the SCADA system at regional power grid scheduling center and the prognoses system of grid dispatching center compares calculates power deviation;

(3), the data obtained according to the SCADA system of regional grid dispatching center and the above-mentioned power deviation parameter of trying to achieve in asking for, utilize Integer programming to find the solution, the electric automobile electrical changing station that draws correction discharges and recharges performance number;

(4), discharge and recharge performance number, be issued to the electric automobile electrical changing station and carry out the plan that discharges and recharges according to the electric automobile electrical changing station of above-mentioned correction.

2. reply wind-powered electricity generation as claimed in claim 1 is the automatic energy storage control method of cutter on a large scale, it is characterized in that: the SCADA system from the regional power grid scheduling center of said step (1) obtains required data and is meant the measured value that obtains each workload demand performance number, each wind energy turbine set wind performance number and each photovoltaic plant output power value of current period from the SCADA system at regional power grid scheduling center; The predicted value of each workload demand performance number, each wind energy turbine set wind performance number and each photovoltaic plant output power value that obtains when before the prognoses system at regional power grid scheduling center obtains, this period being carried out ultrashort phase prediction; Obtaining the maximum of each electric automobile charging station in this period allows charge power value and maximum maximum, minimum inventories electric weight and this period initial time electrical changing station that allows discharge power value, electric automobile energy demand, electrical changing station to allow to store the measured value of electric weight.

3. reply wind-powered electricity generation as claimed in claim 1 is the automatic energy storage control method of cutter on a large scale; It is characterized in that: the predicted value of the data that said step (2) is obtained the SCADA system at regional power grid scheduling center and the prognoses system of grid dispatching center compares to be calculated deviation and is meant; According to the data that step (1) obtains, the deviation the when measured value of asking for network load, wind power and photovoltaic plant output power value was predicted with the ultrashort phase between the predicted value of network load, wind power and photovoltaic plant output power value;

$\mathrm{\ΔP}=\left(\right(\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(o\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(o\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(o\right))-(\underset{d=1}{\overset{D}{\mathrm{\Σ}}}{P}_{\mathrm{lt},d}\left(c\right)-\underset{w=1}{\overset{W}{\mathrm{\Σ}}}{P}_{\mathrm{wt},w}\left(c\right)-\underset{s=1}{\overset{S}{\mathrm{\Σ}}}{P}_{\mathrm{st},s}\left(c\right))---\left(1\right)$

Deviation when Δ P is ultrashort phase prediction between the measured value of the predicted value of network load, wind power and photovoltaic plant output power value and network load, wind power and photovoltaic plant output power value; D, W and S are respectively load, wind energy turbine set, the photovoltaic plant 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 power output of period t _{Wt, w}(o); Ultrashort certain photovoltaic plant s of phase prediction is P in the prediction power output of period t _{St, s}(o); Load d is P at the workload demand measured value of period t _{Lt, d}(c); Wind energy turbine set w is P at the wind-powered electricity generation power output measured value of period t _{Wt, w}(c); Photovoltaic plant s is P at the power output measured value of period t _{St, s}(c).

4. reply wind-powered electricity generation as claimed in claim 1 is the automatic energy storage control method of cutter on a large scale, it is characterized in that: finding the solution of said step (3) is meant:

Target function

$\min {(\mathrm{\ΔP}+(\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(o\right)-\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{d,t,e}\left(o\right))-(\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{\mathrm{ev},t,e}\left(c\right)-\underset{e=1}{\overset{E}{\mathrm{\Σ}}}{P}_{d,t,e}\left(c\right)))}^2---\left(2\right)$

E is the electric automobile electrical changing station number of area power grid; The electric automobile electrical changing station e that before sets in the operation plan is P at plan charge power and the discharge power of this period _{Ev, t, e}(o) and P _{D, t, e}(o), be known quantity; Charging and discharge power value that electric automobile electrical changing station e revised in this period are P _{Ev, t, e}(c) and P _{D, t, e}(c);

Constraints

$0\≤P_{\mathrm{ev},t,e}\left(c\right)\≤X_{1,e}\·P_{\mathrm{ev},t,e}^{\max }\left(c\right)---\left(3\right)$

$0\≤P_{d,t,e}\left(c\right)\≤X_{2,e}\·P_{d,t,e}^{\max }\left(c\right)---\left(4\right)$

P _ev，t，e(c)·X _3，e＝0 (5)

P _d，t，e(c)·X _3，e＝0 (6)

X _1，e+X _2，e+X _3，e＝1 (7)

$S_{t,\mathrm{ev}}\left(c\right)=S_{t-1,\mathrm{ev}}\left(c\right)+{\mathrm{\η}}_{\mathrm{cha},\mathrm{ev}}\·P_{\mathrm{ev},t,e}\left(c\right)\·\mathrm{\Δt}-\frac{\mathrm{\Δt}}{{\mathrm{\η}}_{\mathrm{dch},\mathrm{ev}}}\·P_{d,t,e}\left(c\right)-P_{\mathrm{ev},t,d}\left(c\right)\·\mathrm{\Δt}---\left(8\right)$

$S_e^{\min }\left(c\right)\≤S_{t,\mathrm{ev}}\left(c\right)\≤S_e^{\max }\left(c\right)---\left(9\right)$

With the running status of electric automobile electrical changing station e be divided into electrical network to charging batteries of electric automobile, with batteries of electric automobile in storing electricity to electrical network discharge and idle three kinds of states; X _{1, e}Represented that electric automobile electrical changing station e obtained electric energy from electrical network, X at=1 o'clock _{1, e}Represented that electric automobile electrical changing station e did not obtain electric energy from electrical network at=0 o'clock; X _{2, e}Represented that electric automobile electrical changing station e discharged X to electrical network at=1 o'clock _{2, e}Represented that electric automobile electrical changing station e did not discharge to electrical network at=0 o'clock; X _{3, e}Representing in=1 o'clock does not have energy exchange between electric automobile electrical changing station e and the electrical network, i.e. idle condition, X _{3, e}Represented in=0 o'clock to have energy exchange between electric automobile electrical changing station e and the electrical network, promptly be in busy state;

Be respectively the maximum of electric automobile electrical changing station e and allow charge power and discharge power in this period; η _{Cha, ev}, η _{Dch, ev}Be respectively battery charge efficient and the discharging efficiency of electric automobile electrical changing station e; S _{T-1, ev}(c) be the measured value that this period initial time electrical changing station e stores electric weight; S _{T, ev}(c) be the storage electric weight of this moment period Mo electrical changing station e;

Be maximum, the minimum inventories electric weight of electric automobile electrical changing station e in this period permission; Δ t is the duration of this period; P _{Ev, t, d}(c) Δ t is the electric automobile energy demand and supply electric weight of electric automobile electrical changing station e in this period.

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