CN104966127A - Electric vehicle economic dispatching method based on demand response - Google Patents

Abstract

The invention discloses an electric vehicle economic dispatching method based on demand response. The electric vehicle economic dispatching method comprises the steps of firstly, outlining an economic dispatching model; secondly, proposing an electric vehicle charging strategy based on the demand response, optimizing a charging electricity price trigger value of electric vehicle users based on network real-time electricity price information, reducing charging cost for the users; meanwhile, studying an electric power system unit commitment containing large-scale electric vehicles; and on that basis, establishing an electric vehicle economic dispatching model based on the demand response through the prediction of behavioral features of the electric vehicle users. The invention is aimed at maximizing profits of grid companies, the charging electricity price of an electric vehicle is optimized, and the charging load of the electric vehicle can be shifted.

Description

A kind of electric automobile economic load dispatching method based on demand response

Technical field

The invention belongs to Power System and its Automation technical field, the present invention relates to a kind of electric automobile economic load dispatching method based on demand response more precisely.

Background technology

Along with increasingly sharpening of the energy and environmental crisis, electric automobile, with the advantage of its energy-conserving and environment-protective, becomes one of China's strategy new industry.Extensive electric automobile access electrical network, the planning to electric system, operation are produced profound influence by its unordered discharge and recharge behavior, bring the problems such as the increase of voltage drop, network loss, harmonic pollution.Under the background of intelligent grid development, appropriate charging controls can not only suppress, eliminate the adverse effect of electric automobile to electrical network, and can operation of power networks be supported, reach peak load shifting, assistant service is provided for system, reduce the effects such as system operation cost, electric automobile and electric network coordination are developed.

Current correlative study is greatly mainly with being directly scheduling to basic assumption to electric automobile, in fact, scheduling institution does not have this authority, and along with the expansion of electric automobile scale, also can increase the computation complexity that electric automobile carries out when charging strategy is optimized thereupon, may occur when the electric automobile that scheduling institution directly accesses every platform carries out United Dispatching dimension calamity and computing time long problem.Price type demand response is the important control strategy of the orderly discharge and recharge of electric automobile, and rational Price Mechanisms excitation electric electrical automobile user should be able to select rational time discharge and recharge.

Summary of the invention

Goal of the invention: in order to overcome the deficiencies in the prior art, the invention provides a kind of electric automobile economic load dispatching method based on demand response, there is provided economic load dispatching method for following electric automobile accesses electrical network on a large scale, reduce the adverse effect to electrical network, promote the development of ev industry.

Technical scheme: for achieving the above object, the technical solution used in the present invention is:

Based on an electric automobile economic load dispatching method for demand response, set up the charging electric vehicle strategy based on demand response, according to electrical network Spot Price Advance data quality electric automobile user charged electrical valency trigger value, reduce user's charging cost.Set up the electric automobile economic load dispatching model based on demand response, by the prediction to electric automobile user behavioral trait, turn to target with grid company Income Maximum, optimize and formulate charging electric vehicle electricity price, transfer charging electric vehicle load.

Based on an electric automobile economic load dispatching method for demand response, comprise the steps:

(1) economic load dispatching model is summarized: price type demand response utilizes electricity consumer to the consciousness of price, guide electric automobile user consciously to select to charge in the electrical network electricity price lower period by formulating the dynamic electrical network electrovalence policy changed in time, thus play the effects such as peak load shifting; Under intelligent grid condition, the dynamic electrical network electrovalence policy for electric automobile can reach the degree of Spot Price; For intraday a certain electrical network electricity price curve, in order to reduce charging electric vehicle cost, on the basis meeting charge requirement, selection is charged in the time period that electrical network electricity price is lower by electric automobile user;

(2) the charging electric vehicle strategy based on demand response is proposed: the charging electricity price trigger value p optimizing electric automobile user according to dynamic electrical network electricity price p (t) _set, reduce the charging cost of electric automobile user; The objective function of charging strategy is:

min p _set

(3) research is containing the Power System Unit Commitment of extensive electric automobile: the objective function of Optimization of Unit Commitment By Improved is:

$\min F_G=\underset{t=0}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[C_{\mathrm{Gi}}\left(P_{\mathrm{Gi}}\left(t\right)\right)+S_{\mathrm{Gi}}(1-I_{\mathrm{Gi}}(t-1))]I_{\mathrm{Gi}}\left(t\right)$

In formula: T is the time hop count optimized; N _gfor participating in the fired power generating unit number optimized; C _gi(P _gi(t)) be the cost function of fired power generating unit i, P _git () is fired power generating unit i exerting oneself in the t period; I _gi(t) for fired power generating unit i start shooting within the t period, the binary integer variable of stopped status, I _git ()=1 represents that fired power generating unit i is open state in the t period, I _git ()=0 represents that fired power generating unit i is stopped status in the t period; S _gifor the start expense of fired power generating unit i;

(4) set up the electric automobile economic load dispatching model based on demand response: electric automobile user behavioral trait is predicted, turns to target with grid company Income Maximum, optimize and formulate charging electric vehicle electricity price, shift charging electric vehicle load simultaneously; The objective function of economic load dispatching is:

max F＝S _E-C _E

In formula: F is grid company income, S _efor the charging of electric automobile is taken in, C _efor the charging cost of electric automobile.

Concrete, in described step (2), the charging electric vehicle strategy based on demand response comprises following content:

Going on a journey on the basis of constraint meeting electric automobile user, is the charging cost reducing electric automobile user, according to electrical network electricity price curve, and setting charging electricity price trigger value p _set: when electrical network electricity price is greater than charging electricity price trigger value p _settime, electric automobile stops charging; When electrical network electricity price is less than charging electricity price trigger value p _settime, electric automobile starts charging, and the objective function of charging strategy is:

min p _set

The constraint condition of charging strategy comprises:

(2.1) charged state constraint

In formula: δ (t) is t charging electric vehicle state, δ (t)=1 represents that electric automobile is charged state in t, and δ (t)=0 represents that electric automobile is non-charged state in t; P (t) is t electrical network electricity price; SOC (t) is t electric automobile state-of-charge; SOC _setfor requiring the state-of-charge reached when electric automobile leaves electrical network; t _startfor electric automobile access power grid time; t _endfor electric automobile leaves power grid time;

(2.2) charge power constraint

Setting charging pile is with rated power P _ccharge to electric automobile, charge efficiency is η, then charge power P (t) of t electric automobile is:

P(t)＝δ(t)ηP _C,t _start≤t≤t _end

(2.3) state-of-charge constraint

The state-of-charge of t electric automobile is:

$\mathrm{SOC}\left(t\right)=\mathrm{SOC}(t-1)+\frac{P(t-1)\mathrm{\&Delta;t}}{W_C},t_{\mathrm{start}}<t\&le;t_{\mathrm{end}}$

In formula: W _cfor batteries of electric automobile capacity, △ t is the minimum optimization time interval;

(2.4) user goes out row constraint

SOC(t _end)＝SOC _set

(2.5) required duration of charging constraint

SOC is reached in order to make the state-of-charge of electric automobile _setthe required duration of charging is T _need:

$T_{\mathrm{need}}=\frac{({\mathrm{SOC}}_{\mathrm{set}}-{\mathrm{SOC}}_{\mathrm{atart}})W_C}{\mathrm{\&eta;}{P}_C}$

In formula: SOC _startfor initial state-of-charge during electric automobile access electrical network;

Oneself go owing to allowing electric automobile user to optimize charging electricity price trigger value p _setunrealistic, therefore by optimization charging electricity price trigger value p _setright transfers to charging pile; Whenever having new electric automobile access charging pile, the charge control system of charging pile realizes the orderly charging of electric automobile according to following 3 steps:

1. the charge requirement of electric automobile is obtained

When electric automobile access charging pile, the charge control system of charging pile obtains the state parameter of electric automobile by the battery management system (bms) on electric automobile, comprise quantity of state W _cand SOC _start, and set amount SOC _setand t _end;

2. calculate and can meet charge requirement

The charge control system of charging pile calculates the T met needed for charge requirement according to obtaining information _needif: T _need>t _end-t _start, then judge to meet charge requirement, charging pile should give a warning and allow electric automobile user change t _endor SOC _set, until can charge requirement be met;

3. charging electricity price trigger value p is optimized _set

The charge control system of charging pile reads real-time grid electricity price information, in conjunction with the state parameter optimization charging electricity price trigger value p according to electric automobile _setand charge.

Concrete, in described step (3), research comprises following content containing the Power System Unit Commitment of extensive electric automobile:

Optimization of Unit Commitment By Improved determines each fired power generating unit start/stop time and arrangement of exerting oneself in following certain hour, to make total power production cost minimum, when designing the Power System Unit Commitment containing extensive electric automobile, adopt the objective function identical with conventional rack combinatorial problem (namely thermal power unit operation expense and fired power generating unit start shooting expense sum minimum be objective function), but constraint condition to be expanded; The objective function of Optimization of Unit Commitment By Improved is:

$\min F_G=\underset{t=0}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}[C_{\mathrm{Gi}}\left(P_{\mathrm{Gi}}\left(t\right)\right)+S_{\mathrm{Gi}}(1-I_{\mathrm{Gi}}(t-1))]I_{\mathrm{Gi}}\left(t\right)$

In formula: the cost function of fired power generating unit i wherein, a _i, b _iand c _ifor fired power generating unit i fuel cost coefficient;

The constraint condition of Optimization of Unit Commitment By Improved comprises:

(3.1) account load balancing constraints

The gross capability of the fired power generating unit of all open states should equal total workload demand:

$\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{Gi}}\left(t\right)I_{\mathrm{Gi}}\left(t\right)=P_L\left(t\right)+P_E\left(t\right)$

In formula: P _et () is the workload demand of t period electric automobile, P _lt () is other workload demand of t period;

(3.2) system reserve constraint

$\underset{=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{Gi}}^{\max }\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&GreaterEqual;P_L\left(t\right)+P_E\left(t\right)+R\left(t\right)$

In formula: for fired power generating unit i is in the maximum output of t period, R (t) is for system is in the standby requirement of t period;

(3.3) fired power generating unit exert oneself bound constraint

$P_{\mathrm{Gi}}^{\min }\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&le;P_{\mathrm{Gi}}\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&le;P_{\mathrm{Gi}}^{\max }\left(t\right)I_{\mathrm{Gi}}\left(t\right)$

In formula: for fired power generating unit i is at the minimum load of t period;

(3.4) fired power generating unit startup-shutdown time-constrain

$\left\{\begin{array}{ccc}(X_i^{\mathrm{on}}\left(t\right)-T_i^{\mathrm{on}})(1-I_i(t+1))\&GreaterEqual;0,& \mathrm{if}& I_i\left(t\right)=1\\ (X_i^{\mathrm{off}}\left(t\right)-T_i^{\mathrm{off}})I_i(t+1))\&GreaterEqual;0& \mathrm{if}& I_i\left(t\right)=0\end{array}\right.$

In formula: with be respectively the accumulative on time of fired power generating unit i within the t period and accumulative stop time, with represent the shortest continuous on time that fired power generating unit i allows and the shortest continuous stop time respectively;

(3.5) fired power generating unit Climing constant

Fired power generating unit should meet the restriction of fired power generating unit Climing constant in exerting oneself of adjacent two periods, that is:

$P_{\mathrm{Gi}}\left(t\right)=P_{\mathrm{Gi}}(t-1)\&le;R_u^i$

$P_{\mathrm{Gi}}(t-1)-P_{\mathrm{Gi}}\left(t\right)\&le;R_d^i$

In formula: with be respectively upward slope speed limit value and the descending speed limit value of fired power generating unit i.

Concrete, in described step (4), the electric automobile economic load dispatching model set up based on demand response comprises following content:

If the Unit Commitment cost not containing electric automobile is F ' _g, for tackling the charge requirement of electric automobile, whole Unit Commitment cost is by F ' _gincrease to F _g, its increase volume is the charging cost C of electric automobile _e, that is:

C _E＝F _G-F _G'

By optimizing the charging price curve of electric automobile, guiding electric automobile user to select to charge in the time period that electrical network electricity price is lower, reaching the transfer of charging electric vehicle load; Under a certain grid costs curve, the charging income S of electric automobile _efor:

$S_E=\underset{t=0}{\overset{24}{\mathrm{\&Sigma;}}}{Q}_E\left(t\right)p\left(t\right)\mathrm{\&Delta;t}$

In formula: Q _et () is the charge capacity of t period electric automobile;

In order to simplify problem, by the charging cost C of electric automobile _eapproximate representation is Unit Commitment cost is F _g; Consider the income of grid company, the income of grid company should do not made after implementing dynamic electrical network electricity price p (t) to incur loss, and therefore turn to target with the Income Maximum of grid company, the objective function of economic load dispatching is:

max F＝S _E-C _E

The requirement of side management according to demand, should ensure after carrying out dynamic electrical network electricity price p (t) that electric automobile user interests are not impaired, and namely average electrical network electricity price does not go up, and sets average electrical network electricity tariff constraint condition for this reason:

In formula: for the average charge electricity price of electric automobile after carrying out dynamic electrical network electricity price, for the average charge electricity price of electric automobile before carrying out dynamic electrical network electricity price;

Meanwhile, the scope of dynamic electrical network electricity price p (t) should be between:

p _min≤p(t)≤p _max

In formula: p _minand p _maxbe respectively minimum value and the maximal value of charging electric vehicle electricity price.

Beneficial effect: the electric automobile economic load dispatching method based on demand response provided by the invention, relative to prior art, has following advantage: 1, can according to electrical network Spot Price Advance data quality electric automobile user charged electrical valency trigger value; This charging strategy can ensure the trip requirements meeting electric automobile user, can reduce again the charging cost of electric automobile user simultaneously; 2, the present invention establishes the electric automobile economic load dispatching model based on demand response, charging electric vehicle electricity price is formulated by optimizing, transfer charging load, can reduce the Unit Combination cost of system, is applicable to carry out economic load dispatching to extensive electric automobile access electrical network.

Accompanying drawing explanation

Fig. 1 is method flow diagram of the present invention;

Fig. 2 is electric automobile economic load dispatching basic framework.

Embodiment

Below in conjunction with accompanying drawing, the present invention is further described.

Concrete enforcement based on the electric automobile economic load dispatching method of demand response depends on the understanding to economic load dispatching model framework.To the research of the charging electric vehicle strategy based on demand response, according to electrical network Spot Price Advance data quality electric automobile user charged electrical valency trigger value, reduce user's charging cost.Meanwhile, research is containing the Power System Unit Commitment of extensive electric automobile.On this basis, the electric automobile economic load dispatching model based on demand response is set up.By the prediction to electric automobile user behavioral trait, turn to target with grid company Income Maximum, optimize and formulate charging electric vehicle electricity price, transfer charging electric vehicle load.

Below in conjunction with method flow of the present invention in accompanying drawing 1, the electric automobile economic load dispatching method based on demand response in the present invention is described in detail.

Part I: economic load dispatching model is summarized

Price type demand response utilizes electricity consumer to the consciousness of price, guides electric automobile user consciously to select to charge in the electricity price lower period, thus play the effects such as peak load shifting by formulating the pricing policy changed in time.Under intelligent grid condition, the pricing policy for electric automobile can reach the degree of Spot Price.For intraday a certain price curve, in order to reduce charging electric vehicle cost, on the basis meeting charge requirement, selection is charged in the time period that electricity price is lower by electric automobile user.

Suppose that all charging piles have all installed intelligent terminal, can in real time and electrical network and electric automobile vehicle mounted electric pond energy management system carry out message exchange, obtain electrical network electricity price information and electronic vehicle attitude parameter, and minimum with electric automobile user charging cost be objective optimization charging decision.Power-management centre according to and the information interaction of charging pile, the information such as the state-of-charge can added up the behavioral trait obtaining electric automobile user every day, comprise charging electric vehicle scale, expect when accessing and leave power grid time, initial state-of-charge and leave.When Development of Electric Vehicles is to certain scale, the information of statistics electric automobile can be polymerized business by electric automobile load and have been come, and electric automobile load polymerization business submits to power-management centre information again.Power-management centre according to historical statistical data can to next day electrical network conventional load and next day electric automobile user behavioral trait predict.

Unit Combination is the important component part of power generation dispatching a few days ago, is reply load fluctuation, ensures system reliability and reduce cost of electricity-generating important way.Power-management centre according to genset operation conditions, conventional load predicts the outcome and electric automobile behavioral trait predicts the outcome, by optimizing the charging price curve of electric automobile, electric automobile user is guided to select to charge in the time period that electricity price is lower, reach the transfer of charging electric vehicle load, reduce systems generate electricity cost, form economic load dispatching plan.The basic framework of whole economic load dispatching model as shown in Figure 2.

Part II: propose the charging electric vehicle strategy based on demand response

Going on a journey on the basis of constraint meeting electric automobile user, is the charging cost reducing electric automobile user, according to electrical network electricity price curve, and setting charging electricity price trigger value p _set: when electrical network electricity price is greater than charging electricity price trigger value p _settime, electric automobile stops charging; When electrical network electricity price is less than charging electricity price trigger value p _settime, electric automobile starts charging, and the objective function of charging strategy is:

min p _set

The constraint condition of charging strategy comprises:

(2.1) charged state constraint

In formula: δ (t) is t charging electric vehicle state, δ (t)=1 represents that electric automobile is charged state in t, and δ (t)=0 represents that electric automobile is non-charged state in t; P (t) is t electrical network electricity price; SOC (t) is t electric automobile state-of-charge; SOC _setfor requiring the state-of-charge reached when electric automobile leaves electrical network; t _startfor electric automobile access power grid time; t _endfor electric automobile leaves power grid time;

(2.2) charge power constraint

Setting charging pile is with rated power P _ccharge to electric automobile, charge efficiency is η, then charge power P (t) of t electric automobile is:

P(t)＝δ(t)ηP _C,t _start≤t≤t _end

(2.3) state-of-charge constraint

The state-of-charge of t electric automobile is:

$\mathrm{SOC}\left(t\right)=\mathrm{SOC}(t-1)+\frac{P(t-1)\mathrm{\&Delta;t}}{W_C},t_{\mathrm{start}}<t\&le;t_{\mathrm{end}}$

In formula: W _cfor batteries of electric automobile capacity, △ t is the minimum optimization time interval;

(2.4) user goes out row constraint

SOC(t _end)＝SOC _set

(2.5) required duration of charging constraint

SOC is reached in order to make the state-of-charge of electric automobile _setthe required duration of charging is T _need:

$T_{\mathrm{need}}=\frac{({\mathrm{SOC}}_{\mathrm{set}}-{\mathrm{SOC}}_{\mathrm{atart}})W_C}{\mathrm{\&eta;}{P}_C}$

In formula: SOC _startfor initial state-of-charge during electric automobile access electrical network;

Oneself go owing to allowing electric automobile user to optimize charging electricity price trigger value p _setunrealistic, therefore by optimization charging electricity price trigger value p _setright transfers to charging pile; Whenever having new electric automobile access charging pile, the charge control system of charging pile realizes the orderly charging of electric automobile according to following 3 steps:

1. the charge requirement of electric automobile is obtained

When electric automobile access charging pile, the charge control system of charging pile obtains the state parameter of electric automobile by the battery management system (bms) on electric automobile, comprise quantity of state W _cand SOC _start, and set amount SOC _setand t _end;

2. calculate and can meet charge requirement

The charge control system of charging pile calculates the T met needed for charge requirement according to obtaining information _needif: T _need>t _end-t _start, then judge to meet charge requirement, charging pile should give a warning and allow electric automobile user change t _endor SOC _set, until can charge requirement be met;

3. charging electricity price trigger value p is optimized _set

The charge control system of charging pile reads real-time grid electricity price information, in conjunction with the state parameter optimization charging electricity price trigger value p according to electric automobile _setand charge.

Part III: research is containing the Power System Unit Commitment of extensive electric automobile

Optimization of Unit Commitment By Improved determines each fired power generating unit start/stop time and arrangement of exerting oneself in following certain hour, to make total power production cost minimum, when designing the Power System Unit Commitment containing extensive electric automobile, adopt the objective function identical with conventional rack combinatorial problem (namely thermal power unit operation expense and fired power generating unit start shooting expense sum minimum be objective function), but constraint condition to be expanded; The objective function of Optimization of Unit Commitment By Improved is:

$\min F_G=\underset{t=0}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}[C_{\mathrm{Gi}}\left(P_{\mathrm{Gi}}\left(t\right)\right)+S_{\mathrm{Gi}}(1-I_{\mathrm{Gi}}(t-1))]I_{\mathrm{Gi}}\left(t\right)$

In formula: the cost function of fired power generating unit i wherein, a _i, b _iand c _ifor fired power generating unit i fuel cost coefficient;

The constraint condition of Optimization of Unit Commitment By Improved comprises:

(3.1) account load balancing constraints

The gross capability of the fired power generating unit of all open states should equal total workload demand:

$\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{Gi}}\left(t\right)I_{\mathrm{Gi}}\left(t\right)=P_L\left(t\right)+P_E\left(t\right)$

In formula: P _et () is the workload demand of t period electric automobile, P _lt () is other workload demand of t period;

(3.2) system reserve constraint

$\underset{=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{Gi}}^{\max }\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&GreaterEqual;P_L\left(t\right)+P_E\left(t\right)+R\left(t\right)$

In formula: for fired power generating unit i is in the maximum output of t period, R (t) is for system is in the standby requirement of t period;

(3.3) fired power generating unit exert oneself bound constraint

$P_{\mathrm{Gi}}^{\min }\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&le;P_{\mathrm{Gi}}\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&le;P_{\mathrm{Gi}}^{\max }\left(t\right)I_{\mathrm{Gi}}\left(t\right)$

In formula: for fired power generating unit i is at the minimum load of t period;

(3.4) fired power generating unit startup-shutdown time-constrain

$\left\{\begin{array}{ccc}(X_i^{\mathrm{on}}\left(t\right)-T_i^{\mathrm{on}})(1-I_i(t+1))\&GreaterEqual;0,& \mathrm{if}& I_i\left(t\right)=1\\ (X_i^{\mathrm{off}}\left(t\right)-T_i^{\mathrm{off}})I_i(t+1))\&GreaterEqual;0& \mathrm{if}& I_i\left(t\right)=0\end{array}\right.$

In formula: with be respectively the accumulative on time of fired power generating unit i within the t period and accumulative stop time, with represent the shortest continuous on time that fired power generating unit i allows and the shortest continuous stop time respectively;

(3.5) fired power generating unit Climing constant

Fired power generating unit should meet the restriction of fired power generating unit Climing constant in exerting oneself of adjacent two periods, that is:

$P_{\mathrm{Gi}}\left(t\right)=P_{\mathrm{Gi}}(t-1)\&le;R_u^i$

$P_{\mathrm{Gi}}(t-1)-P_{\mathrm{Gi}}\left(t\right)\&le;R_d^i$

In formula: with be respectively upward slope speed limit value and the descending speed limit value of fired power generating unit i.

Part IV: set up the electric automobile economic load dispatching model based on demand response

If the Unit Commitment cost not containing electric automobile is F ' _g, for tackling the charge requirement of electric automobile, whole Unit Commitment cost is by F ' _gincrease to F _g, its increase volume is the charging cost C of electric automobile _e, that is:

C _E＝F _G-F′ _G

By optimizing the charging price curve of electric automobile, guiding electric automobile user to select to charge in the time period that electrical network electricity price is lower, reaching the transfer of charging electric vehicle load; Under a certain grid costs curve, the charging income S of electric automobile _efor:

$S_E=\underset{t=0}{\overset{24}{\mathrm{\&Sigma;}}}{Q}_E\left(t\right)p\left(t\right)\mathrm{\&Delta;t}$

In formula: Q _et () is the charge capacity of t period electric automobile;

In order to simplify problem, by the charging cost C of electric automobile _eapproximate representation is Unit Commitment cost is F _g; Consider the income of grid company, the income of grid company should do not made after implementing dynamic electrical network electricity price p (t) to incur loss, and therefore turn to target with the Income Maximum of grid company, the objective function of economic load dispatching is:

max F＝S _E-C _E

The requirement of side management according to demand, should ensure after carrying out dynamic electrical network electricity price p (t) that electric automobile user interests are not impaired, and namely average electrical network electricity price does not go up, and sets average electrical network electricity tariff constraint condition for this reason:

In formula: for the average charge electricity price of electric automobile after carrying out dynamic electrical network electricity price, for the average charge electricity price of electric automobile before carrying out dynamic electrical network electricity price;

Meanwhile, the scope of dynamic electrical network electricity price p (t) should be between:

p _min≤p(t)≤p _max

In formula: p _minand p _maxbe respectively minimum value and the maximal value of charging electric vehicle electricity price.

To sum up, the electric automobile economic load dispatching method based on demand response provided by the invention according to electrical network Spot Price Advance data quality electric automobile user charged electrical valency trigger value, can reduce user's charging cost.Establish the electric automobile economic load dispatching model based on demand response, by the prediction to electric automobile user behavioral trait, turn to target with grid company Income Maximum, optimize and formulate charging electric vehicle electricity price, transfer charging electric vehicle load.Economic load dispatching theoretical foundation is provided for following electric automobile accesses electrical network on a large scale.

The above is only the preferred embodiment of the present invention; be noted that for those skilled in the art; under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.

Claims (4)

1., based on an electric automobile economic load dispatching method for demand response, it is characterized in that: comprise the steps:

(1) economic load dispatching model is summarized: price type demand response utilizes electricity consumer to the consciousness of price, guide electric automobile user consciously to select to charge in the electrical network electricity price lower period by formulating the dynamic electrical network electrovalence policy changed in time, thus play the effects such as peak load shifting; Under intelligent grid condition, the dynamic electrical network electrovalence policy for electric automobile can reach the degree of Spot Price; For intraday a certain electrical network electricity price curve, in order to reduce charging electric vehicle cost, on the basis meeting charge requirement, selection is charged in the time period that electrical network electricity price is lower by electric automobile user;

(2) the charging electric vehicle strategy based on demand response is proposed: the charging electricity price trigger value p optimizing electric automobile user according to dynamic electrical network electricity price p (t) _set, reduce the charging cost of electric automobile user; The objective function of charging strategy is:

min p _set

(3) research is containing the Power System Unit Commitment of extensive electric automobile: the objective function of Optimization of Unit Commitment By Improved is:

$\min F_G=\underset{t=0}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}[C_{\mathrm{Gi}}\left(P_{\mathrm{Gi}}\left(t\right)\right)+S_{\mathrm{Gi}}(1-I_{\mathrm{Gi}}(t-1))]I_{\mathrm{Gi}}\left(t\right)$

In formula: T is the time hop count optimized; N _gfor participating in the fired power generating unit number optimized; C _gi(P _gi(t)) be the cost function of fired power generating unit i, P _git () is fired power generating unit i exerting oneself in the t period; I _gi(t) for fired power generating unit i start shooting within the t period, the binary integer variable of stopped status, I _git ()=1 represents that fired power generating unit i is open state in the t period, I _git ()=0 represents that fired power generating unit i is stopped status in the t period; S _gifor the start expense of fired power generating unit i;

(4) set up the electric automobile economic load dispatching model based on demand response: electric automobile user behavioral trait is predicted, turns to target with grid company Income Maximum, optimize and formulate charging electric vehicle electricity price, shift charging electric vehicle load simultaneously; The objective function of economic load dispatching is:

max F＝S _E-C _E

In formula: F is grid company income, S _efor the charging of electric automobile is taken in, C _efor the charging cost of electric automobile.

2. the electric automobile economic load dispatching method based on demand response according to claim 1, it is characterized in that: in described step (2), the charging electric vehicle strategy based on demand response comprises following content:

Going on a journey on the basis of constraint meeting electric automobile user, is the charging cost reducing electric automobile user, according to electrical network electricity price curve, and setting charging electricity price trigger value p _set: when electrical network electricity price is greater than charging electricity price trigger value p _settime, electric automobile stops charging; When electrical network electricity price is less than charging electricity price trigger value p _settime, electric automobile starts charging, and the objective function of charging strategy is:

min p _set

The constraint condition of charging strategy comprises:

(2.1) charged state constraint

In formula: δ (t) is t charging electric vehicle state, δ (t)=1 represents that electric automobile is charged state in t, and δ (t)=0 represents that electric automobile is non-charged state in t; P (t) is t electrical network electricity price; SOC (t) is t electric automobile state-of-charge; SOC _setfor requiring the state-of-charge reached when electric automobile leaves electrical network; t _startfor electric automobile access power grid time; t _endfor electric automobile leaves power grid time;

(2.2) charge power constraint

Setting charging pile is with rated power P _ccharge to electric automobile, charge efficiency is η, then charge power P (t) of t electric automobile is:

P(t)＝δ(t)ηP _C,t _start≤t≤t _end

(2.3) state-of-charge constraint

The state-of-charge of t electric automobile is:

$\mathrm{SOC}\left(t\right)=\mathrm{SOC}(t-1)+\frac{P(t-1)\mathrm{\&Delta;t}}{W_C},t_{\mathrm{start}}<t\&le;t_{\mathrm{end}}$

In formula: W _cfor batteries of electric automobile capacity, △ t is the minimum optimization time interval;

(2.4) user goes out row constraint

SOC(t _end)＝SOC _set

(2.5) required duration of charging constraint

SOC is reached in order to make the state-of-charge of electric automobile _setthe required duration of charging is T _need:

$T_{\mathrm{need}}=\frac{({\mathrm{SOC}}_{\mathrm{set}}-{\mathrm{SOC}}_{\mathrm{start}})W_C}{{\mathrm{\&eta;P}}_C}$

In formula: SOC _startfor initial state-of-charge during electric automobile access electrical network;

Oneself go owing to allowing electric automobile user to optimize charging electricity price trigger value p _setunrealistic, therefore by optimization charging electricity price trigger value p _setright transfers to charging pile; Whenever having new electric automobile access charging pile, the charge control system of charging pile realizes the orderly charging of electric automobile according to following 3 steps:

1. the charge requirement of electric automobile is obtained

When electric automobile access charging pile, the charge control system of charging pile obtains the state parameter of electric automobile by the battery management system (bms) on electric automobile, comprise quantity of state W _cand SOC _start, and set amount SOC _setand t _end;

2. calculate and can meet charge requirement

The charge control system of charging pile calculates the T met needed for charge requirement according to obtaining information _needif: T _need>t _end-t _start, then judge to meet charge requirement, charging pile should give a warning and allow electric automobile user change t _endor SOC _set, until can charge requirement be met;

3. charging electricity price trigger value p is optimized _set

The charge control system of charging pile reads real-time grid electricity price information, in conjunction with the state parameter optimization charging electricity price trigger value p according to electric automobile _setand charge.

3. the electric automobile economic load dispatching method based on demand response according to claim 1, is characterized in that: in described step (3), and research comprises following content containing the Power System Unit Commitment of extensive electric automobile:

Optimization of Unit Commitment By Improved determines each fired power generating unit start/stop time and arrangement of exerting oneself in following certain hour, to make total power production cost minimum, when designing the Power System Unit Commitment containing extensive electric automobile, adopt the objective function identical with conventional rack combinatorial problem, but constraint condition is expanded; The objective function of Optimization of Unit Commitment By Improved is:

$\min F_G=\underset{t=0}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}[C_{\mathrm{Gi}}\left(P_{\mathrm{Gi}}\left(t\right)\right)+S_{\mathrm{Gi}}(1-I_{\mathrm{Gi}}(t-1))]I_{\mathrm{Gi}}\left(t\right)$

In formula: the cost function of fired power generating unit i wherein, a _i, b _iand c _ifor fired power generating unit i fuel cost coefficient;

The constraint condition of Optimization of Unit Commitment By Improved comprises:

(3.1) account load balancing constraints

The gross capability of the fired power generating unit of all open states should equal total workload demand:

$\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{Gi}}\left(t\right)I_{\mathrm{Gi}}\left(t\right)=P_L\left(t\right)+P_E\left(t\right)$

In formula: P _et () is the workload demand of t period electric automobile, P _lt () is other workload demand of t period;

(3.2) system reserve constraint

$\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{Gi}}^{\max }\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&GreaterEqual;P_L\left(t\right)+P_E\left(t\right)+R\left(t\right)$

In formula: for fired power generating unit i is in the maximum output of t period, R (t) is for system is in the standby requirement of t period;

(3.3) fired power generating unit exert oneself bound constraint

$P_{\mathrm{Gi}}^{\min }\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&le;P_{\mathrm{Gi}}\left(t\right)I_{\mathrm{Gi}}\left(t\right)\&le;P_{\mathrm{Gi}}^{\max }\left(t\right)I_{\mathrm{Gi}}\left(t\right)$

In formula: for fired power generating unit i is at the minimum load of t period;

(3.4) fired power generating unit startup-shutdown time-constrain

$\left\{\begin{array}{ccc}(X_i^{\mathrm{on}}\left(t\right)-T_i^{\mathrm{on}})(1-I_i(t+1))\&GreaterEqual;0& ,\mathrm{if}& I_i\left(t\right)=1\\ (X_i^{\mathrm{off}}\left(t\right)-T_i^{\mathrm{off}})I_i(t+1))\&GreaterEqual;0& ,\mathrm{if}& I_i\left(t\right)=0\end{array}\right.$

In formula: with be respectively the accumulative on time of fired power generating unit i within the t period and accumulative stop time, with represent the shortest continuous on time that fired power generating unit i allows and the shortest continuous stop time respectively;

(3.5) fired power generating unit Climing constant

Fired power generating unit should meet the restriction of fired power generating unit Climing constant in exerting oneself of adjacent two periods, that is:

$P_{\mathrm{Gi}}\left(t\right)-P_{\mathrm{Gi}}(t-1)\&le;R_u^i$

$P_{\mathrm{Gi}}(t-1)-P_{\mathrm{Gi}}\left(t\right)\&le;R_d^i$

In formula: with be respectively upward slope speed limit value and the descending speed limit value of fired power generating unit i.

4. the electric automobile economic load dispatching method based on demand response according to claim 1, is characterized in that: in described step (4), and the electric automobile economic load dispatching model set up based on demand response comprises following content:

If the Unit Commitment cost not containing electric automobile is F ' _g, for tackling the charge requirement of electric automobile, whole Unit Commitment cost is by F ' _gincrease to F _g, its increase volume is the charging cost C of electric automobile _e, that is:

C _E＝F _G-F′ _G

By optimizing the charging price curve of electric automobile, guiding electric automobile user to select to charge in the time period that electrical network electricity price is lower, reaching the transfer of charging electric vehicle load; Under a certain grid costs curve, the charging income S of electric automobile _efor:

$S_E=\underset{t=0}{\overset{24}{\mathrm{\&Sigma;}}}{Q}_E\left(t\right)p\left(t\right)\mathrm{\&Delta;t}$

In formula: Q _et () is the charge capacity of t period electric automobile;

In order to simplify problem, by the charging cost C of electric automobile _eapproximate representation is Unit Commitment cost is F _g; Consider the income of grid company, the income of grid company should do not made after implementing dynamic electrical network electricity price p (t) to incur loss, and therefore turn to target with the Income Maximum of grid company, the objective function of economic load dispatching is:

max F＝S _E-C _E

The requirement of side management according to demand, should ensure after carrying out dynamic electrical network electricity price p (t) that electric automobile user interests are not impaired, and namely average electrical network electricity price does not go up, and sets average electrical network electricity tariff constraint condition for this reason:

In formula: for the average charge electricity price of electric automobile after carrying out dynamic electrical network electricity price, for the average charge electricity price of electric automobile before carrying out dynamic electrical network electricity price;

Meanwhile, the scope of dynamic electrical network electricity price p (t) should be between:

p _min≤p(t)≤p _max

In formula: p _minand p _maxbe respectively minimum value and the maximal value of charging electric vehicle electricity price.