CN103632205B - A kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling - Google Patents

Abstract

The invention discloses a kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, with the power system containing can network hybrid-electric car, wind-powered electricity generation and thermoelectricity as object of study, build meter and probabilistic energy-saving and emission-reduction scheduling model；First using many scenario simulations technology that probabilistic stochastic process is decomposed into some typical discrete probabilistic scenes, employing multi-agent system technology will be with energy-conservation and CO₂Discharge is decomposed into 24 job agency for the Optimized Operation of target, use genetic algorithm for solving collection, Collaborative Agent is responsible between job agency Dynamic Coupling scheduling, makes system meet dynamic equilibrium constraint, eventually through weight regulation realize carbon emission and energy-conservation between effective coordination.The model that the present invention is set up is effective and feasible, and PHEV can effectively realize the peak load that disappears of load, promotes receiving of wind-powered electricity generation, plays the effect of load scheduling, and the weight of carbon emission target energy-conservation by Rational choice, can realize energy-conservation and the most compromise between reducing discharging.

Description

A kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling

Technical field

The invention belongs to power system optimal dispatch field, relate to wind-powered electricity generation and process with negative rules, electronic vapour The charge and discharge control of car, particularly relates to probabilistic process.

Background technology

Along with energy scarcity and environmental pollution contradiction, clean reproducible energy receives significant attention.Electricity Electrical automobile energy-saving and emission-reduction, containment climate warming and ensure the oil supply aspect such as safely have orthodox car without The advantage of method analogy, by the extensive concern of national governments, car manufactures and energy enterprise.The a U.S. The technical report that National Renewable Energy Laboratory is given shows that PHEV can reduce the discharge of CO2 in a large number.Just Owing to PHEV has huge energy-saving and emission-reduction potentiality, its research and extension has become the focus that various countries pay close attention to.PHEV energy No it is effectively used in the utilization depending on regenerative resource to a certain extent.Carry out positive charging (V2G) to control, The charge capacity of electric automobile is provided by regenerative resource completely, forms regenerative resource the most mutual with PHEV Mend.Can realize charge load regenerative resource is exerted oneself change tracking, promote regenerative resource receive.

Under traditional mode, the Optimized Operation containing wind-powered electricity generation and electric automobile does not consider the uncertainty of wind-powered electricity generation and load, All use Deterministic Methods to solve, do not correspond with the randomness characteristic of wind-powered electricity generation, load, it is impossible to truly reflect reality Border dispatch situation.Many scenario simulations technology is used to be decomposed into some by the stochastic process of wind-powered electricity generation, negative rules Typical probability scene so that it is relatively accurately reflect the electric power Optimized Operation stochastic process containing electric automobile and wind-powered electricity generation. Then, use multi-agent system technology that 1 day 24 period was corresponded to 24 job agency, responsible thermoelectricity, Static scheduling between wind-powered electricity generation and electric automobile, coordinating agent is responsible for the dynamic coordinate between 24 job agency, So that energy-optimised utilizes wind-powered electricity generation and electric automobile, make scheduling result closer to reality.

Summary of the invention

In order to make up the defect of traditional mode, the present invention propose a kind of consider wind-powered electricity generation and negative rules containing electricity Electrical automobile Optimized Operation strategy, with containing networking hybrid-electric car (hereinafter referred to as PHEV), wind-powered electricity generation and fire The power system of electricity is object of study, constructs meter and probabilistic energy-saving and emission-reduction scheduling model, considers The uncertainty of wind-powered electricity generation and load, the charge and discharge control of PHEV, the harmonizing of PHEV and wind-powered electricity generation.First adopt By many scenario simulations technology, probabilistic stochastic process is decomposed into some typical discrete probabilistic scenes, at this On the basis of use multi-agent system technology (hereinafter referred to as MAS) technology Optimized Operation is divided into 24 work generations Managing, job agency is responsible for the static scheduling of each period, and coordinating agent is responsible for Dynamic Coupling between job agency and is adjusted Degree, eventually through weight regulation achieve carbon emission and energy-conservation between effective coordination.

It is an object of the invention to be achieved through the following technical solutions:

A kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, comprise the steps:

(1) the following 24 hours workload demand data of system that electrical network machine unit scheduling center draws are accepted；Receive wind The electric field prediction data to wind power output size, the bound including prediction wind-powered electricity generation size and wind power output is interval； Receive the correlation properties data of PHEV；Each unit is drawn according to the machine unit characteristic data that each power plant reports Characteristic constraint；

(2) exert oneself according to Large Scale Wind Farm Integration, the unascertained information of load, use many scenario simulations technology to select Take some typical system running states to use and carbon emission Optimized Operation for systematic running cost, can relatively accurately reflect Electric power Optimized Operation containing PHEV and wind-powered electricity generation.

(3) data received according to first and second step and some typical scenes, to power system optimal dispatch It is modeled, selects object function and constraints according to service requirement, including equality constraint and inequality Constraints, constitutes mixed integer nonlinear programming problem；

(4) the mixed integer nonlinear programming problem produced according to previous step, draws consideration wind-powered electricity generation and load not Deterministic containing electric automobile energy saving reduction of discharging multiple objective function and corresponding constraints.Constraints is: containing PHEV System power Constraints of Equilibrium, the Constraints of Equilibrium of PHEV, containing PHEV spinning reserve constraint, PHEV discharge and recharge Total amount constraint, the exerting oneself of fired power generating unit self, climb, the constraint such as minimum startup-shutdown.

(5) by the energy-saving and emission-reduction multiple target of previous step by composing weight to each target, multi-objective problem is turned Turn to new single-objective problem, and regulate each target importance in energy-saving and emission-reduction general objective by weight.

(6) whole day is divided into 24 periods, within 1 hour, is 1 scheduling slot, by 24 job agency A1-A24 Being responsible for, the most each period corresponds to 1 job agency, and each job agency inner utilization genetic algorithm is asked Solve, then by Collaborative Agent, the solution of 24 job agency is coordinated, finally give Optimized Operation in a day Solution.

The invention has the beneficial effects as follows: the present invention is to considering that uncertainty is lower containing PHEV and the energy-saving and emission-reduction of wind-powered electricity generation Scheduling strategy is studied, and establishes containing PHEV and wind-powered electricity generation probabilistic energy-saving and emission-reduction model.And for negative Lotus and the randomness of wind power output, use many scenario simulations technology by discrete to load and wind-powered electricity generation variable for some typical cases Scene, then use MAS technology to solve the scheduling problem of discretization.In MAS, job agency is responsible for each scheduling In period, the regulation that thermoelectricity is exerted oneself, wind-powered electricity generation and the complementary scheduling of PHEV, the coordination of wind-powered electricity generation, thermoelectricity and PHEV. Coordinating agent is responsible for coordinating 24 job agency, it is achieved dynamic Optimized Operation.Example shows, is set up Model is effective and feasible, and PHEV can effectively realize the peak load that disappears of load, promotes receiving of wind-powered electricity generation, plays negative The effect of lotus scheduling.The weight of and carbon emission target energy-conservation by Rational choice, can realize energy-conservation and between reducing discharging Effectively compromise.

Accompanying drawing explanation

Fig. 1 is the flow chart of the present invention；

Fig. 2 is the scene distribution figure of wind-powered electricity generation；

Fig. 3 is the scene distribution figure of load；

Fig. 4 is the MAS system assumption diagram of Optimized Operation strategy；

Fig. 5 is the synergism schematic diagram of job agency.

Detailed description of the invention

Below in conjunction with the accompanying drawings the present invention is described in further detail.

A kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimized Operation strategy, comprise the steps:

(1) the following 24 hours workload demand data of system that electrical network machine unit scheduling center draws are accepted；Receive wind The electric field prediction data to wind power output size, the bound including prediction wind-powered electricity generation size and wind power output is interval； Receive the correlation properties data of PHEV；Each machine is drawn according to the machine unit characteristic data that each power plant reports The characteristic constraint of group；

(2) exert oneself according to Large Scale Wind Farm Integration, the unascertained information of load, use many scenario simulations technology to select Take some typical system running states to use and carbon emission Optimized Operation for systematic running cost, can relatively accurately reflect Electric power Optimized Operation containing PHEV and wind-powered electricity generation.

(3) data received according to first and second step and some typical scenes, to power system optimal dispatch It is modeled, selects object function and constraints according to service requirement, including equality constraint and inequality Constraints, constitutes mixed integer nonlinear programming problem；

(4) the mixed integer nonlinear programming problem produced according to previous step, draws consideration wind-powered electricity generation and load not Deterministic containing electric automobile energy saving reduction of discharging multiple objective function and corresponding constraints.Constraints is: contain The system power Constraints of Equilibrium of PHEV, the Constraints of Equilibrium of PHEV, the spinning reserve constraint containing PHEV, PHEV The constraint of discharge and recharge total amount, the exerting oneself of fired power generating unit self, climb, the constraint such as minimum startup-shutdown.

(5) by the energy-saving and emission-reduction multiple target of previous step by composing weight to each target, multi-objective problem is turned Turn to new single-objective problem, and regulate each target importance in energy-saving and emission-reduction general objective by weight.

(6) whole day is divided into 24 periods, within 1 hour, is 1 scheduling slot, by 24 job agency A1-A24 Being responsible for, the most each period corresponds to 1 job agency, and each job agency inner utilization genetic algorithm is asked Solve, then by Collaborative Agent, the solution of 24 job agency is coordinated, finally give Optimized Operation in a day Solution.

Below in conjunction with accompanying drawing, elaborating embodiments of the invention, the flow chart of the present invention is as shown in Figure 1.

The discrete probability distribution collection of load and wind power output is represented by:

${\mathrm{\δ}}_D=\{({p_d}^1,{\mathrm{\ρ}}_d^1);({p_d}^2,{\mathrm{\ρ}}_d^2);...({p_d}^s,{\mathrm{\ρ}}_d^s);...({p_d}^{nd},{\mathrm{\ρ}}_d^{nd})\}---\left(1\right)$

For uncertain load load value under scene s and the probability of correspondence thereof, nd is load Scene sum.

${\mathrm{\ρ}}_d^1+{\mathrm{\ρ}}_d^2+...+{\mathrm{\ρ}}_d^{nd}=1---\left(2\right)$

${\mathrm{\δ}}_w=\{(p_{wind}^1,{\mathrm{\ρ}}_{\mathrm{\ω}}^1);(p_{wind}^2,{\mathrm{\ρ}}_{\mathrm{\ω}}^2);\·\·(p_{wind}^s,{\mathrm{\ρ}}_{\mathrm{\ω}}^s);\·\·(p_{wind}^{nw},{\mathrm{\ρ}}_{\mathrm{\ω}}^{nw})\}---\left(3\right)$

For the uncertain wind-powered electricity generation probability going out force value and correspondence thereof under scene s.Nw is wind-powered electricity generation Scene sum.

${\mathrm{\ρ}}_{\mathrm{\ω}}^1+{\mathrm{\ρ}}_{\mathrm{\ω}}^2+...+{\mathrm{\ρ}}_{\mathrm{\ω}}^{nw}=1---\left(4\right)$

The collection of load and all scenes of wind-powered electricity generation share SC and represents,

SC=δ_D×δ_w (5)

$\underset{s\∈SC}{\mathrm{\Σ}}{\mathrm{\ρ}}_d{\mathrm{\ρ}}_{\mathrm{\ω}}=1---\left(6\right)$

δ_D、δ_wIt is respectively load and the set of wind-powered electricity generation Discrete Distribution, ρ_d、ρ_ωIt is respectively load and wind-powered electricity generation not Probability in the case of determining.ρ_sFor system probability under scene s.

ρ_s=ρ_dρ_w (7)

Assume that wind-powered electricity generation, load error obey standard normal distribution, and according to general by wind-powered electricity generation and load of statistical data Rate distribution be divided into 5 scenes, the scene distribution of wind-powered electricity generation and load see Fig. 2,3.

δ_w={ (p_w× 100%, 0.5)；(p_d× 99%, 0.15)；(p_d× 101%, 0.15)；

(p_d× 97.5%, 0.1)；(p_d× 102.5%, 0.1) (8)

δ_D={ (p_d× 100%, 0.6)；(p_d× 98.5%, 0.15)；(p_d× 102%, 0.15)；

(p_d× 98%, 0.05)；(p_d× 103%, 0.05) (9)

p_w, p_dFor wind-powered electricity generation, the value of load prediction.

Corresponding object function is:

Use many scenario simulations technology, it is considered to probabilistic fuel cost function of load and wind-powered electricity generation develops into:

$\[{\mathrm{FC}}_i\left(P_i^t\right),{\mathrm{\ρ}}_s\]=\[a_i+b_i{p}_i^{st}+c_i{\left(p_i^{st}\right)}^2,{\mathrm{\ρ}}_s\]---\left(10\right)$

And ρ_sIt is unit i output and probability of its correspondence under scene s.A in formula_i、b_i、c_iFor machine The consumption characterisitic parameter of group i, unit is respectively $/h, $/MWh, $/MW²h。

Fired power generating unit carbon emission amount is expressed as the quadratic function form of unit output,

$\[E_{ci}\left(P_i^{st}\right),{\mathrm{\ρ}}_s\]=\[({\mathrm{\α}}_{ci}+{\mathrm{\β}}_{ci}{p}_i^{st}+{\mathrm{\γ}}_{ci}{\left(p_i^{st}\right)}^2)u_i^t,{\mathrm{\ρ}}_s\]---\left(11\right)$

α_ci, β_ci, γ_ciCO for unit i₂Discharge function coefficients, unit is ton/h, ton/MWh, ton/MW²h。

Therefore consider energy-saving and emission-reduction and uncertain Optimal Operation Model be:

$\begin{array}{c}min\{\underset{s\∈S}{\mathrm{\Σ}}{\mathrm{\ρ}}_s\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\[W_c(a_i+b_i{p}_i^{st}+c_i{\left(p_i^{st}\right)}^2)u_i^t+S_i{u}_i^t\\ (1-u_i^{t-1})+W_e({\mathrm{\α}}_{ci}+{\mathrm{\β}}_{ci}{p}_i^{st}+{\mathrm{\γ}}_{ci}{\left(p_i^{st}\right)}^2)u_i^t\]\}\end{array}---\left(12\right)$

In formulaRepresent that unit is in operation/stopped status；S_iStart-up cost for unit；Hop count when T is, N is unit number.W_cAnd W_eIt is operating cost and the weight corresponding to carbon emission.

W_c+W_e=1； (13)

Corresponding constraints is:

1) the system power Constraints of Equilibrium containing PHEV

PHEV discharges

PHEV charges

2) Constraints of Equilibrium of PHEV

$\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{N}_{v2G}^t=N_{v2G}^{\max },t=1,2,...,T$

It is total for PHEV,Quantity for t PHEV.

3) the spinning reserve constraint containing PHEV

PHEV discharges

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{u}_i^t{p}_{imax}+p_v^{\max }{N}_{v2G}^t\≥p_d^t+R^t,t=1,2,...,T$

PHEV charges

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{u}_i^t{p}_{imax}\≥p_d^t+p_v^{\max }{N}_{v2G}^t+R^t,t=1,2,...,T$

p_imaxThe maximum exerted oneself for fired power generating unit,Capacity for each PHEV.

4) PHEV discharge and recharge total amount constraint

$N_{v2G}^t\≤N_{v2G}^{\max t},t=1,2,...,T$

Owing to all of PHEV can not be in synchronization discharge and recharge simultaneously, in order to ensure the safe and reliable of system Running, the discharge and recharge quantity controlling PHEV is necessary,For t period PHEV maximum charge-discharge Sum.

5) unit output bound constraint

$p_{i\min }\≤p_i^t\≤p_{imax}---\left(5\right)$

6) fired power generating unit minimax units limits

$p_{i\min }\≤p_i^t\≤p_{imax}---\left(8\right)$

7) fired power generating unit Climing constant

When unit liter is exerted oneself

$p_i^t-p_i^{t-1}\≤{\mathrm{UR}}_i---\left(9\right)$

When unit fall is exerted oneself

$p_i^{t-1}-p_i^t\≤{\mathrm{DR}}_i---\left(10\right)$

8) fired power generating unit minimum startup-shutdown time-constrain

$\left\{\begin{array}{cc}{u}_i^t=1,& 1\&le;X_i^t<M_{GTi}\\ u_i^t=0,& -M_{DTi}<X_i^t\&le;-1\end{array}\right.---\left(11\right)$

In formula: p_imax、p_iminMaximum, minimum load for fired power generating unit i；R^tHold for t period system spinning reserve Amount；UR_iAnd DR_iIt is respectively unit i active power ascending amount and the limit value of slippage；For unit i to the t period Run continuously (For on the occasion of) or shut down continuously (For negative value) time hop count；M_GTi、M_DTiIt is respectively unit i Minimum operation time and minimum downtime.Fig. 4 is the MAS system assumption diagram of Optimized Operation strategy；Fig. 5 It it is the synergism schematic diagram of job agency.

In sum, it is proposed that consider wind-powered electricity generation and negative rules containing electric automobile Optimized Operation plan Slightly, use many scenario simulations technology and multi-agent system technology, real in the case of wind-powered electricity generation and negative rules Show the carbon emission amount that is greatly decreased, and effectively realized the peak load that disappears of load, promoted receiving of wind-powered electricity generation, played The effect of load scheduling.

It should be noted that in flow chart or any process described otherwise above or method describe permissible at this It is understood to, represents and include one or more performing for the step that realizes specific logical function or process The module of code, fragment or the part of instruction, and the scope of the preferred embodiments of the invention includes other Realize, wherein can not by order that is shown or that discuss, including according to involved function by basic simultaneously Mode or in the opposite order, performs function, and this should be by the technology of embodiments of the invention art Personnel are understood.

In sum, the present invention is that one uses many scenario simulations technology and multi-agent system technology to realize examining Consider wind-powered electricity generation and the Optimized Operation containing electric automobile of negative rules.This technology can be used for other uncertain problems Process, such as the uncertainty optimization scheduling containing other new forms of energy such as solar energy, the present invention is with containing PHEV, wind The power system of electricity and thermoelectricity is object of study, constructs meter and probabilistic energy-saving and emission-reduction scheduling model, combines Close and consider the uncertainty of wind-powered electricity generation and load, the charge and discharge control of hybrid-electric car (PHEV) that can network, PHEV and the harmonizing of wind-powered electricity generation.Multi-scenario technique is used to be decomposed into some typical cases by accurate for stochastic process Discrete scene.The harmonizing of wind-powered electricity generation, electric automobile and thermoelectricity is realized subsequently by multi-agent system technology, real Existing energy saving of system maximizes with reduction of discharging and social benefit, more tallies with the actual situation.

The ultimate principle of the present invention, principal character and advantage have more than been shown and described.The technical staff of the industry It should be appreciated that the present invention is not restricted to the described embodiments, simply saying described in above-described embodiment and description The principle of the bright present invention, without departing from the spirit and scope of the present invention, the present invention also has various change And improvement, these changes and improvements both fall within scope of the claimed invention.Claimed scope Defined by appending claims and equivalent thereof.

Claims (7)

1. one kind consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, it is characterized in that: with the power system containing can network hybrid-electric car, wind-powered electricity generation and thermoelectricity as object of study, build meter and probabilistic energy-saving and emission-reduction scheduling model, consider wind-powered electricity generation and the uncertainty of load, the charge and discharge control of the hybrid-electric car that can network, the harmonizing of can network hybrid-electric car and wind-powered electricity generation；First using many scenario simulations technology that probabilistic stochastic process is decomposed into some typical discrete probabilistic scenes, on this basis, employing multi-agent system technology will be with energy-conservation and CO₂Discharge is decomposed into 24 job agency for the Optimized Operation of target, and job agency is responsible for the static scheduling of each period, uses genetic algorithm for solving collection；Collaborative Agent is responsible for Dynamic Coupling scheduling between job agency, makes system meet dynamic equilibrium constraint；Eventually through weight regulation realize carbon emission and energy-conservation between effective coordination；Specifically include following steps:

(1) the following 24 hours workload demand data of system that electrical network machine unit scheduling center draws are received；Receiving the wind energy turbine set prediction data to wind power output size, the bound including prediction wind-powered electricity generation size and wind power output is interval；Reception can network the correlation properties data of hybrid-electric car；The machine unit characteristic data reported according to each power plant draw the characteristic constraint of each unit；

(2) exert oneself according to Large Scale Wind Farm Integration, the unascertained information of load, use many scenario simulations technology choose some typical system running states for systematic running cost with and carbon emission Optimized Operation；

Described many scenario simulations technology uses discrete probability distribution to replace the uncertainty of stochastic variable, and the generation of scene is through 2 steps: 1) obtained the probability distribution of stochastic variable by probabilistic method；2) method using approximation, on the premise of reducing information loss as far as possible, by the former probability distribution discretization of stochastic variable；

The discrete probability distribution set representations of load and wind power output is:

For uncertain load load value under scene s and the probability of correspondence thereof, nd is the scene sum of load；

For the uncertain wind-powered electricity generation probability going out force value and correspondence thereof under scene s, nw is the scene sum of wind-powered electricity generation；

The collection of load and all scenes of wind-powered electricity generation share SC and represents,

SC=δ_D×δ_w (5)

δ_D、δ_wIt is respectively load and the set of wind-powered electricity generation Discrete Distribution, ρ_d、ρ_ωIt is respectively load and wind-powered electricity generation probability under uncertain condition, ρ_sFor system probability under scene s；

ρ_s=ρ_dρ_w (7)

(3) data received according to first and second step and some typical scenes, power system optimal dispatch is modeled, select object function and constraints according to service requirement, including equality constraint and inequality constraints condition, constitute mixed integer nonlinear programming problem；

(4) the mixed integer nonlinear programming problem produced according to previous step, show that consider wind-powered electricity generation and negative rules reduces discharging multiple objective function and corresponding constraints containing electric automobile energy saving；

(5) by the energy-saving and emission-reduction multiple objective function of previous step by composing weight to each target, multi-objective problem is converted into new single-objective problem, and regulates each target importance in energy-saving and emission-reduction general objective by weight；

(6) whole day is divided into 24 periods, it within 1 hour, it is 1 scheduling slot, it is responsible for by 24 job agency A1-A24, the most each period corresponds to 1 job agency, each job agency inner utilization genetic algorithm solves, then by Collaborative Agent, the solution of 24 job agency is coordinated, the solution of Optimized Operation in finally giving a day.

The most according to claim 1 a kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, it is characterized in that: the reduction of discharging multiple objective function containing electric automobile energy saving of described consideration wind-powered electricity generation and negative rules uses many scenario simulations technology, it is considered to probabilistic fuel cost function of load and wind-powered electricity generation develops into:

And ρ_sIt is unit i output and the probability of its correspondence, a in formula under scene s_i、b_i、c_iFor the consumption characterisitic parameter of unit i, unit is respectively $/h, $/MWh, $/MW²H, fired power generating unit carbon emission amount is expressed as the quadratic function form of unit output,

α_ci, β_ci, γ_ciCO for unit i₂Discharge function coefficients, unit is ton/h, ton/MWh, ton/MW²h。

The most according to claim 1 a kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling; it is characterized in that: the constraints containing electric automobile energy saving reduction of discharging of consideration wind-powered electricity generation and negative rules is: containing the system power Constraints of Equilibrium of the hybrid-electric car that can network; can be networked the Constraints of Equilibrium of hybrid-electric car; spinning reserve constraint containing the hybrid-electric car that can network; the hybrid-electric car discharge and recharge total amount that can network retrains, and the exerting oneself of fired power generating unit self, climb, minimum startup-shutdown constraint.

The most according to claim 1 a kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, it is characterised in that: described step (5) considers energy-saving and emission-reduction and uncertain Optimal Operation Model is:

In formulaRepresent that unit is in operation/stopped status；S_iStart-up cost for unit；Hop count when T is, N is unit number, W_cAnd W_eIt is operating cost and the weight corresponding to carbon emission,

W_c+W_e=1； (11).

The most according to claim 1 a kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, it is characterized in that: each job agency of described multi-agent system technology is in addition to existing contact with Collaborative Agent, also the most adjacent with it agency carries out communication for information, for ease of difference, the agency of previous period is called the forerunner of current agent, what the agency of a rear period was current agent is follow-up, and Collaborative Agent reaches the target of systematic collaboration evolution.

The most according to claim 1 a kind of consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, it is characterized in that: each described job agency is responsible for coordinating wind-powered electricity generation, thermoelectricity and the static scheduling that can network between hybrid-electric car, the general objective that its target is the operating cost in this period and gas emissions is minimum, constraints is the static constraint condition under corresponding load section, and do not consider the start-stop time Dynamic Coupling constraint of unit, use genetic algorithm to try to achieve a disaggregation.

7. according to a kind of described in claims 1 consider wind-powered electricity generation and negative rules containing electric automobile Optimization Scheduling, it is characterised in that: the target of described Collaborative Agent be the fuel consumption in whole dispatching cycle and gas total emission volumn minimum, constraint.