CN106096790A - Based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets Optimization Scheduling - Google Patents

Abstract

The invention discloses a kind of based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets Optimization Scheduling, initially set up frequency-conversion air-conditioning system model；Introduce air-conditioning state-of-charge parameter SOC, set up the relation between air conditioning system current SOC state and power adjustment, obtain the computing formula of electrical power maximum adjustment amount under minimax electrical power and SOC state constraint；For the air-conditioning group that parameter is different, with the k means algorithm in cluster analysis, air-conditioning group is divided into several groups according to parameter similarity, the air-conditioning parameter of each group thinks identical, and same type of air-conditioning group is carried out polymerization modeling, the power sum that aggregate power is several groups of air-conditioning groups of the most whole air-conditioning group.The virtual plant based on the modeling of convertible frequency air-conditioner virtual robot arm that the present invention provides a few days ago with Real-time markets Optimization Scheduling, it is possible to take into full account that the parameter difference opposite sex of air-conditioning group carries out United Dispatching to air-conditioning group, fully excavate air-conditioning group and participate in demand response potentiality.

Description

Based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method

Technical field

The present invention relates to a kind of virtual plant based on the modeling of convertible frequency air-conditioner virtual robot arm adjust with Real-time markets optimization a few days ago Degree method, belongs to demand response technology application technology in virtual plant, is specifically related to the polymerization of the different air-conditioning group of parameter Modeling, control and virtual plant a few days ago with the Optimized Operation in Real-time markets.

Background technology

Demand response technology is one of core technology of intelligent grid, alleviates supply and demand tension, increasing by demand response Strong system reply current rip kinetic force, raising running efficiency of system, minimizing relevant enterprise have lost and have maximized economic interests Become industry common cognition.In all flexible loads, thermal control load has hot storage capacity can be within a certain period of time because of it Transfer load can be that system provides multiple assistant service to receive extensive concern.Air conditioner load is that a kind of typical thermal control is born Lotus, its compressor experienced by from determining the frequency development to frequency conversion, and current convertible frequency air-conditioner is because of its higher efficiency share in the market It is gradually increased.This patent uses centralized Control method to regulate and control air-conditioning group, and air conditioner load has a very wide distribution, the scale of construction Greatly, therefore need certain technological means that it is carried out polymerization modeling, facilitate United Dispatching and the control of relevant departments.Therefore, grind Polymerization modeling and the control technique of studying carefully convertible frequency air-conditioner have preferable application prospect.

Along with a large amount of regenerative resources access electrical network, the safety and stability economical operation of electrical network is by the biggest threat, virtual Power plant can integrate the resources such as various distributed power source, load, energy storage, by aggregating into a virtual controlled aggregation, participates in Operation of power networks scheduling and Electricity Market Operation, coordinating between intelligent grid and distributed power source while contradiction, improving entirety Economic benefit.

Summary of the invention

Goal of the invention: in order to overcome the deficiencies in the prior art, the present invention provides a kind of virtual based on convertible frequency air-conditioner Unit modeling virtual plant a few days ago with Real-time markets Optimization Scheduling, with realize to extensive air conditioner load concentrate regulation and control, And improve the economic benefit of virtual robot arm.

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

A kind of based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets Optimization Scheduling, including Following steps:

(1) thermodynamical model and the electrical model of single air conditioner is set up according to conservation of energy principle and operation of air conditioner characteristic, I.e. set up the relation between the power P of air-conditioning and refrigerating capacity Q of air-conditioning；

(2) introduce state-of-charge parameter SOC of air-conditioning, set up the pass between state-of-charge parameter SOC and power adjustment System, obtains peak power P of air-conditioning_maxValue and minimum power P_minMaximum rise under value, and the constraint of state-of-charge parameter SOC PowerAdjusting power under computing formula and maximumComputing formula；With peak power P_max, minimum power P_min, maximum raise PowerCalculating parameter, maximum lower adjusting powerCalculating parameter characterization single air conditioner model, i.e. with parameter characterization separate unit Air-conditioning model；

(3) with the k-means algorithm in cluster analysis, air-conditioning group is divided into a K group according to parameter similarity, unifies each All air-conditionings in each packet are carried out polymerization modeling by the parameter of air-conditioning in packet；

(4) set up the virtual robot arm model of air-conditioning group, according to the operation characteristic of virtual plant, build virtual robot arm and be scheduled to This；

(5) in ahead market, according to wind power prediction situation next day, with virtual plant maximizing the benefits for target letter Number, optimizes virtual robot arm next day and exerts oneself；

(6) it is to reduce virtual plant economic loss in Real-time markets further, according to wind power and outdoor temperature Prediction case, exerts oneself to virtual robot arm and carries out rolling optimization, it is achieved virtual plant maximization of economic benefit；

(7) after virtual robot arm receives dispatch command, take into account user fairness and comfort level, optimize load adjustment amount at air-conditioning Distribution in Qun.

Concrete, described step (1) comprises the steps:

(11) thermodynamical model of single air conditioner is set up:

$C_a\frac{{\mathrm{dT}}_{in}}{dt}=\frac{1}{R_1}(T_{out}-T_{in})+Q^{\′}-Q---\left(1\right)$

Wherein: T_outFor outdoor temperature, T_inFor indoor temperature, C_aFor the equivalent thermal capacitance of air-conditioning, R₁Equivalence resistance for air-conditioning Anti-, Q is the refrigerating capacity of air-conditioning, and Q' is the heat dissipation capacity of indoor object, and t is the time；

(12) electrical model of single air conditioner is set up:

By the relational representation of the power P of air-conditioning and frequency f of air-conditioning it is:

P=k₁f+l₁ (2)

By the relational representation of refrigerating capacity Q of air-conditioning and frequency f of air-conditioning it is:

Q=k₂f+l₂ (3)

The relation between the power P of air-conditioning and refrigerating capacity Q of air-conditioning of setting up is:

$Q=\frac{k_2}{k_1}P+\frac{k_1{l}_2-l_1{k}_2}{k_1}---\left(4\right)$

Wherein: k₁、l₁、k₂And l₂It is constant coefficient.

Concrete, described step (2) comprises the steps:

Air-conditioning is to be stored in affiliated building with the form of heat energy by electric energy, and the highest energy storage capacity of indoor temperature is the least, room The lowest energy storage capacity of interior temperature is the biggest, and the comfort level scope of note user is [T_min,T_max]；If indoor temperature is T_maxTime energy storage capacity be 0, then indoor temperature is T_inTime energy storage capacity O_inFor:

O_i=C_a(T_max-T_in) (5)

The stored energy capacitance O of building is:

O=C_a(T_max-T_min) (6)

State-of-charge parameter SOC of definition air-conditioning is energy storage capacity O_inRatio with stored energy capacitance O:

$SOC=\frac{O_{in}}{O}=\frac{T_{max}-T_{in}}{T_{\max }-T_{\min }}---\left(7\right)$

Regulation and control initial time was designated as 0 moment；

Formula (7) is brought into formula (1) and obtains the time-varying variance of state-of-charge parameter SOC:

$\frac{dSOC}{dt}=-\frac{1}{R_1{C}_a}SOC\left(t\right)-\frac{T_{out}-T_{\max }}{R_1{C}_a(T_{\max }-T_{\min })}-\frac{Q^{\′}-Q\left(t\right)}{C_a(T_{\max }-T_{\min })}---\left(8\right)$

When indoor temperature maintains T_inTime, the refrigerating capacity of its correspondence with the relational expression of indoor temperature is:

$Q=\frac{1}{R_1}(T_{out}-T_{in})+Q^{\′}---\left(9\right)$

The relation that can obtain initial time refrigerating capacity and state-of-charge parameter according to formula (7) and (9) is:

$Q\left(0\right)=\frac{T_{out}-T_{max}+R_1{Q}^{\′}}{R_1}+\frac{T_{max}-T_{\min }}{R_1}SOC\left(0\right)---\left(10\right)$

Can obtain according to formula (8) and (10):

$\frac{dSOC\left(t\right)}{dt}=-\frac{1}{R_1{C}_a}\left(SOC\right(t)-SOC(0\left)\right)+\frac{1}{C_a(T_{max}-T_{\min })}\left(Q\right(t)-Q(0\left)\right)---\left(11\right)$

Wherein: Q (0) and Q (t) is respectively 0 moment and the refrigerating capacity of t air-conditioning, when SOC (0) and SOC (t) is respectively 0 Carve and the state-of-charge parameter of t air-conditioning；

Convolution (4), the relation that can obtain between state-of-charge parameter SOC and the power P of air-conditioning of air-conditioning is:

$\frac{dSOC\left(t\right)}{dt}=-\frac{1}{R_1{C}_a}\left(SOC\right(t)-SOC(0\left)\right)+\frac{k_2}{C_a{k}_1(T_{\max }-T_{\min })}\left(P\right(t)-P(0\left)\right)---\left(12\right)$

$SOC\left(t\right)=SOC\left(0\right)+\frac{k_2{R}_1}{k_1(T_{max}-T_{\min })}(1-e^{-\frac{\mathrm{\Δ}t}{R_1{C}_a}})\left(P\right(t)-P(0\left)\right)---\left(13\right)$

Wherein: P (0) and P (t) is respectively 0 moment and the power of t air-conditioning；

Obtain state-of-charge parameter SOC of air-conditioning and power rise amount P of air-conditioning^upWith power decreasing amount P^downCalculating close System is:

$\left\{\begin{array}{c}SOC\left(t\right)=SOC\left(0\right)+\frac{k_2{R}_1}{k_1(T_{\max }-T_{\min })}(1-e^{-\frac{t}{C_a{R}_1}})P^{up}\\ SOC\left(t\right)=SOC\left(0\right)-\frac{k_2{R}_1}{k_1(T_{\max }-T_{\min })}(1-e^{-\frac{t}{C_a{R}_1}})P^{down}\end{array}\right.---\left(14\right)$

When the air-conditioning regulation and control cycle is Δ t, when if desired raising the power of air-conditioning, according to the operation characteristic of convertible frequency air-conditioner, Power rise amount P of air-conditioning^upNeed to meet and retrain as follows:

$\left\{\begin{array}{c}SOC\left(t\right)\≤1\\ P^{up}\≤P_{\max }-P\left(0\right)\end{array}\right.---\left(15\right)$

Obtain adjusting power in maximumComputing formula is:

$P_{\max }^{up}=\min \left(\mathrm{\κ}\right(1-SOC\left(0\right)),P_{max}-\mathrm{\δ}SOC(0)+\mathrm{\χ}-{\mathrm{\ξT}}_{out})---\left(16\right)$

When the air-conditioning regulation and control cycle is Δ t, when if desired lowering the power of air-conditioning, according to the operation characteristic of convertible frequency air-conditioner, Power decreasing amount P of air-conditioning^downNeed to meet and retrain as follows:

$\left\{\begin{array}{c}SOC\left(t\right)\≥0\\ P^{down}\≤P\left(0\right)-P_{\min }\end{array}\right.---\left(17\right)$

Obtain adjusting power under maximumComputing formula is:

$P_{\max }^{down}=\min \left(\mathrm{\κ}SOC\right(0),\mathrm{\δ}SOC(0)-\mathrm{\χ}+{\mathrm{\ξT}}_{out}-P_{\min })---\left(18\right)$

Wherein:

$\mathrm{\κ}=\frac{k_1(T_{max}-T_{\min })}{R_1{k}_2(1-e^{-\frac{\mathrm{\Δ}t}{R_1{C}_a}})}---\left(19\right)$

$\mathrm{\ξ}=\frac{k_1}{R_1{k}_2}---\left(20\right)$

$\mathrm{\χ}=\frac{k_1{l}_2{R}_1-k_2{l}_1{R}_1-k_1{Q}^{\′}{R}_1+k_1{T}_{\max }}{R_1{k}_2}---\left(21\right)$

$\mathrm{\δ}=\frac{k_1(T_{max}-T_{\min })}{R_1{k}_2}---\left(22\right)$

With parameter sets { κ ξ χ δ P_max P_minCharacterize an air-conditioning.

Concrete, described step (3) comprises the steps:

First, with the k-means algorithm in cluster analysis, air-conditioning group is divided into a K group according to parameter similarity, unified each The parameter of air-conditioning in individual packet, in kth packet, all air-conditionings all use { κ_k ξ_k χ_k δ_k P_max,k P_min,kRepresent；

Then, the excursion [0,1] of state-of-charge parameter SOC is divided into N number of minizone, according to the lotus of every air-conditioning All air-conditionings in each packet are divided in each minizone by electricity condition parameter SOC, and it is each little that statistics kth is grouped Air-conditioning quantity in interval is respectively m_k1,m_k2,…,m_ki,…,m_kN, by the state-of-charge parameter of the air-conditioning in i-th minizone Unified for SOC_i:

${\mathrm{SOC}}_i=\frac{1}{N}i-\frac{1}{2N}---\left(23\right)$

Calculate the air-conditioned maximum rise general power of the institute in the i-th minizone of kth packetLower with maximum General powerIt is respectively as follows:

$P_{\max ,ki}^{up}=m_{ki}{P}_{\max ,k}^{up}\left({\mathrm{SOC}}_i\right)---\left(24\right)$

$P_{\max ,ki}^{down}=m_{ki}{P}_{\max ,k}^{down}\left({\mathrm{SOC}}_i\right)---\left(25\right)$

Wherein:For kth packet i-th minizone in each air-conditioning maximum on adjusting power,For adjusting power under the maximum of each air-conditioning in the i-th minizone of kth packet；

The maximum of whole air-conditioning group raises general powerGeneral power is lowered with maximumIt is respectively as follows:

$P_{\max \_total}^{up}=\underset{k=1}{\overset{K}{\Σ}}\underset{i=1}{\overset{N}{\Σ}}{P}_{\max ,ki}^{up}---\left(26\right)$

$P_{\max \_total}^{down}=\underset{k=1}{\overset{K}{\Σ}}\underset{i=1}{\overset{N}{\Σ}}{P}_{\max ,ki}^{down}---\left(27\right)$

Wherein:Maximum for whole air-conditioning group raises general power,Maximum downward for whole air-conditioning group General power.

Concrete, described step (4) comprises the steps:

Before not calling virtual robot arm, the load income of electricity charge F that virtual plant obtains^ahFor:

F^ah=λ_aP_L (28)

After calling virtual robot arm, the load income of electricity charge F that virtual plant obtains^afFor:

F^af=λ_a(P_L+P_G) (29)

Virtual plant is to the reimbursement for expenses F of virtual robot arm₁ ^VGFor:

$F_1^{VG}=\left\{\begin{array}{cc}{\mathrm{\&lambda;}}_b{P}_G& P_G\&GreaterEqual;0\\ -{\mathrm{\&lambda;}}_b{P}_G& P_G<0\end{array}\right.---\left(30\right)$

Income difference before and after virtual plant schedule virtual unit is defined as the scheduling cost F into virtual robot arm^VG:

F^VG=F^ah-(F^af-F₁ ^VG) (31)

Calculate the unit scheduling cost λ of virtual robot arm_vFor:

${\mathrm{\&lambda;}}_v=\frac{F^{VG}}{\left|P_G\right|}={\mathrm{\&lambda;}}_b-{\mathrm{\&lambda;}}_a\frac{P_G}{\left|P_G\right|}---\left(32\right)$

Wherein: λ_vUnit for virtual robot arm dispatches cost, λ_bThe load compensation unit price of user is paid for virtual robot arm, λ_aFor the electricity consumption unit price of user, P_GFor the total activation power of virtual robot arm, P_LFor loading.

Concrete, described step (5) comprises the steps:

In ahead market, according to wind power prediction situation next day, with virtual plant maximizing the benefits as object function, Optimizing virtual robot arm next day to exert oneself, object function is:

$\max F_1=\underset{i=1}{\overset{H}{\&Sigma;}}\left(F_{VPP}\right(i)+F_L(i)-F_{RES}(i)-F_{VG}(i\left)\right)---\left(33\right)$

Wherein:

F_VPP(i)=λ_c(i)(P_WG(i)+P_G(i)-P_L(i)) (34)

F_L(i)=λ_aP_L(i) (35)

F_RES(i)=λ_aP_WG(i) (36)

F_VG(i)=λ_vP_G(i) (37)

Wherein: hop count when H is every day total；F₁For virtual plant at the total revenue of ahead market, F_VPPI () is virtual electricity Factory is at the sale of electricity income of the i-th period of ahead market, F_LI () is the virtual plant load electricity charge income in the i-th period of ahead market, F_RESI () is that virtual plant buys the cost of electricity, F in the i-th period of ahead market from wind energy turbine set_VGI () is that virtual plant is a few days ago I-th period of market pays the compensation of virtual robot arm and spends, λ_cI () is ahead market next day the i-th period cleaing price of prediction, P_WGI () is the wind power of i-th period of next day of prediction, P_LI () is the loading of i-th period of next day of prediction, P_GI () is pre- The total activation power of the i-th period of the next day virtual robot arm surveyed, P_GI () is decision variable；

Constraints is:

$-P_{\max \_total}^{down}\left(i\right)\&le;P_G\left(i\right)\&le;P_{\max \_total}^{up}\left(i\right)---\left(38\right)$

Wherein:For prediction the i-th period of next day virtual robot arm maximum under adjusting power,For Adjusting power in the maximum of the i-th period of next day virtual robot arm of prediction.

Concrete, described step (6) comprises the steps:

In Real-time markets, for reducing the economy that virtual plant brings in Real-time markets due to wind-powered electricity generation uncertainty in traffic Loss, exerts oneself to virtual robot arm according to wind power and outdoor temperature prediction case and carries out rolling optimization, it is achieved virtual plant warp Ji maximizing the benefits, object function is:

$\max F_2=\underset{i=1}{\overset{H}{\&Sigma;}}\left(P_{VPP}^{rt}\right(i)-P_{VPP}^a(i\left)\right){\mathrm{\&lambda;}}^{rt}\left(i\right)---\left(39\right)$

$P_{VPP}^{rt}\left(i\right)=P_{WG}\left(i\right)+P_G\left(i\right)-P_L\left(i\right)---\left(40\right)$

${\mathrm{\&lambda;}}^{rt}\left(i\right)=\left\{\begin{array}{cc}{\mathrm{\&rho;}}^{up}{\mathrm{\&lambda;}}_c\left(i\right)& P_{VPP}^{rt}\left(i\right)\&le;P_{VPP}^a\left(i\right)\\ {\mathrm{\&rho;}}^{down}{\mathrm{\&lambda;}}_c\left(i\right)& P_{VPP}^{rt}\left(i\right)>P_{VPP}^a\left(i\right)\end{array}\right.---\left(41\right)$

$\left\{\begin{array}{c}{\mathrm{\&rho;}}^{up}\&GreaterEqual;1\\ {\mathrm{\&rho;}}^{down}<1\end{array}\right.---\left(42\right)$

Wherein: F₂For virtual plant in the total revenue of Real-time markets,It is that the i-th period virtual plant is in Real-time markets Electricity sales amount,It is the i-th period virtual plant ahead market bid amount in Real-time markets, λ^rtI () is the injustice of prediction The electricity price of weighing apparatus the i-th period of market, ρ^upFor the rise electricity price ratio in uneven market, ρ^downDownward electricity price ratio for uneven market Rate.

Concrete, described step (7) comprises the steps:

After virtual robot arm receives dispatch command, take into account user fairness and comfort level, optimize load adjustment amount air-conditioning group In distribution:

$P_G^k=P_G\frac{\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}{\underset{k=1}{\overset{K}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}---\left(43\right)$

$P_G^{ki}=P_G^k\frac{P_{max,ki}}{\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}---\left(44\right)$

Wherein: work as P_GWhen >=0,Work as P_GDuring ＜ 0,P_GTotal activation merit for virtual robot arm Rate,Air-conditioned total activation power in being grouped for kth,For all of sky in the i-th minizone of kth packet The total activation power adjusted.

Beneficial effect: the virtual plant based on the modeling of convertible frequency air-conditioner virtual robot arm that the present invention provides is a few days ago and Real-time markets Optimization Scheduling, it is possible to take into full account that the parameter difference opposite sex of air-conditioning group carries out United Dispatching to air-conditioning group, fully excavate air-conditioning Group participates in demand response potentiality；In order to realize the effective control to air-conditioning group further, air-conditioning group is equivalent to a virtual robot arm, Build the cost function of virtual robot arm, participate in a few days ago dispatching with Real-time markets of virtual plant.

Accompanying drawing explanation

Fig. 1 is the general flow chart of the inventive method；

Fig. 2 is the polymerization model schematic diagram of same type air-conditioning group；

Fig. 3 is ahead market virtual plant Scheduling Framework.

Detailed description of the invention

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

It is illustrated in figure 1 a kind of virtual plant based on the modeling of convertible frequency air-conditioner virtual robot arm to adjust with Real-time markets optimization a few days ago Degree flow process, is illustrated with regard to each step below

Step one: set up the thermodynamical model of single air conditioner and electric mould according to conservation of energy principle and operation of air conditioner characteristic Type, i.e. sets up the relation between the power P of air-conditioning and refrigerating capacity Q of air-conditioning.

Set up the thermodynamical model of single air conditioner:

$C_a\frac{{\mathrm{dT}}_{in}}{dt}=\frac{1}{R_1}(T_{out}-T_{in})+Q^{\&prime;}-Q---\left(1\right)$

Wherein: T_outFor outdoor temperature, T_inFor indoor temperature, C_aFor the equivalent thermal capacitance of air-conditioning, R₁Equivalence resistance for air-conditioning Anti-, Q is the refrigerating capacity of air-conditioning, and Q' is the heat dissipation capacity of indoor object, and t is the time；

Set up the electrical model of single air conditioner:

By the relational representation of the power P of air-conditioning and frequency f of air-conditioning it is:

P=k₁f+l₁ (2)

By the relational representation of refrigerating capacity Q of air-conditioning and frequency f of air-conditioning it is:

Q=k₂f+l₂ (3)

The relation between the power P of air-conditioning and refrigerating capacity Q of air-conditioning of setting up is:

$Q=\frac{k_2}{k_1}P+\frac{k_1{l}_2-l_1{k}_2}{k_1}---\left(4\right)$

Wherein: k₁、l₁、k₂And l₂It is constant coefficient.

Step 2: introduce state-of-charge parameter SOC of air-conditioning, set up between state-of-charge parameter SOC and power adjustment Relation, obtain peak power P of air-conditioning_maxValue and minimum power P_minMaximum under value, and the constraint of state-of-charge parameter SOC Upper adjusting powerAdjusting power under computing formula and maximumComputing formula；With peak power P_max, minimum power P_min, maximum Upper adjusting powerCalculating parameter, maximum lower adjusting powerCalculating parameter characterization single air conditioner model, i.e. with parameter characterization Single air conditioner model.

Air-conditioning is to be stored in affiliated building with the form of heat energy by electric energy, and the highest energy storage capacity of indoor temperature is the least, room The lowest energy storage capacity of interior temperature is the biggest, and the comfort level scope of note user is [T_min,T_max]；If indoor temperature is T_maxTime energy storage capacity be 0, then indoor temperature is T_inTime energy storage capacity O_inFor:

O_i=C_a(T_max-T_in) (5)

The stored energy capacitance O of building is:

O=C_a(T_max-T_min) (6)

State-of-charge parameter SOC of definition air-conditioning is energy storage capacity O_inRatio with stored energy capacitance O:

$SOC=\frac{O_{in}}{O}=\frac{T_{max}-T_{in}}{T_{\max }-T_{\min }}---\left(7\right)$

Regulation and control initial time was designated as 0 moment；

Formula (7) is brought into formula (1) and obtains the time-varying variance of state-of-charge parameter SOC:

$\frac{dSOC\left(t\right)}{dt}=-\frac{1}{R_1{C}_a}SOC\left(t\right)-\frac{T_{out}-T_{\max }}{R_1{C}_a(T_{\max }-T_{\min })}-\frac{Q^{\&prime;}-Q\left(t\right)}{C_a(T_{\max }-T_{\min })}---\left(8\right)$

When indoor temperature maintains T_in, its corresponding refrigerating capacity with the relational expression of indoor temperature is

$Q=\frac{1}{R_1}(T_{out}-T_{in})+Q^{\&prime;}---\left(9\right)$

The relation that can obtain initial time refrigerating capacity and state-of-charge parameter according to formula (7) and (9) is:

$Q\left(0\right)=\frac{T_{out}-T_{max}+R_1{Q}^{\&prime;}}{R_1}+\frac{T_{max}-T_{\min }}{R_1}SOC\left(0\right)---\left(10\right)$

Can obtain according to formula (8) and (10):

$\frac{dSOC\left(t\right)}{dt}=-\frac{1}{R_1{C}_a}\left(SOC\right(t)-SOC(0\left)\right)+\frac{1}{C_a(T_{max}-T_{\min })}\left(Q\right(t)-Q(0\left)\right)---\left(11\right)$

Wherein: Q (0) and Q (t) is respectively 0 moment and the refrigerating capacity of t air-conditioning, when SOC (0) and SOC (t) is respectively 0 Carve and the state-of-charge parameter of t air-conditioning；

Convolution (4), the relation that can obtain between state-of-charge parameter SOC and the power P of air-conditioning of air-conditioning is:

$\frac{dSOC\left(t\right)}{dt}=-\frac{1}{R_1{C}_a}\left(SOC\right(t)-SOC(0\left)\right)+\frac{k_2}{C_a{k}_1(T_{\max }-T_{\min })}\left(P\right(t)-P(0\left)\right)---\left(12\right)$

$SOC\left(t\right)=SOC\left(0\right)+\frac{k_2{R}_1}{k_1(T_{\max }-T_{\min })}(1-e^{-\frac{\mathrm{\&Delta;}t}{R_1{C}_a}})\left(P\right(t)-P(0\left)\right)---\left(13\right)$

Wherein: P (0) and P (t) is respectively 0 moment and the power of t air-conditioning；

Obtain state-of-charge parameter SOC of air-conditioning and power rise amount P of air-conditioning^upWith power decreasing amount P^downCalculating close System is:

$\left\{\begin{array}{c}SOC\left(t\right)=SOC\left(0\right)+\frac{k_2{R}_1}{k_1(T_{\max }-T_{\min })}(1-e^{-\frac{t}{C_a{R}_1}})P^{up}\\ SOC\left(t\right)=SOC\left(0\right)-\frac{k_2{R}_1}{k_1(T_{\max }-T_{\min })}(1-e^{-\frac{t}{C_a{R}_1}})P^{down}\end{array}\right.---\left(14\right)$

When the air-conditioning regulation and control cycle is Δ t, when if desired raising the power of air-conditioning, according to the operation characteristic of convertible frequency air-conditioner, Power rise amount P of air-conditioning^upNeed to meet and retrain as follows:

$\left\{\begin{array}{c}SOC\left(t\right)\&le;1\\ P^{up}\&le;P_{max}-P\left(0\right)\end{array}\right.---\left(15\right)$

Obtain adjusting power in maximumComputing formula is:

$P_{\max }^{up}=\min \left(\mathrm{\&kappa;}\right(1-SOC\left(0\right)),P_{max}-\mathrm{\&delta;}SOC(0)+\mathrm{\&chi;}-{\mathrm{\&xi;T}}_{out})---\left(16\right)$

When the air-conditioning regulation and control cycle is Δ t, when if desired lowering the power of air-conditioning, according to the operation characteristic of convertible frequency air-conditioner, Power decreasing amount P of air-conditioning^downNeed to meet and retrain as follows:

$\left\{\begin{array}{c}SOC\left(t\right)\&GreaterEqual;0\\ P^{down}\&le;P\left(0\right)-P_{\min }\end{array}\right.---\left(17\right)$

Obtain adjusting power under maximumComputing formula is:

$P_{max}^{down}=\min \left(\mathrm{\&kappa;}SOC\right(0),\mathrm{\&delta;}SOC(0)-\mathrm{\&chi;}+{\mathrm{\&xi;T}}_{out}-P_{\min })---\left(18\right)$

Wherein:

$\mathrm{\&kappa;}=\frac{k_1(T_{max}-T_{\min })}{R_1{k}_2(1-e^{-\frac{\mathrm{\&Delta;}t}{R_1{C}_a}})}---\left(19\right)$

$\mathrm{\&xi;}=\frac{k_1}{R_1{k}_2}---\left(20\right)$

$\mathrm{\&chi;}=\frac{k_1{l}_2{R}_1-k_2{l}_1{R}_1-k_1{Q}^{\&prime;}{R}_1+k_1{T}_{max}}{R_1{k}_2}---\left(21\right)$

$\mathrm{\&delta;}=\frac{k_1(T_{max}-T_{\min })}{R_1{k}_2}---\left(22\right)$

With parameter sets { κ ξ χ δ P_max P_minCharacterize an air-conditioning.

Step 3: air-conditioning group is divided into a K group according to parameter similarity with the k-means algorithm in cluster analysis, unified All air-conditionings in each packet are carried out polymerization modeling by the parameter of air-conditioning in each packet.

First, with the k-means algorithm in cluster analysis, air-conditioning group is divided into a K group according to parameter similarity, unified each The parameter of air-conditioning in individual packet, in kth packet, all air-conditionings all use { κ_k ξ_k χ_k δ_k P_max,k P_min,kRepresent；

Then, the excursion [0,1] of state-of-charge parameter SOC is divided into N number of minizone, according to the lotus of every air-conditioning All air-conditionings in each packet are divided in each minizone by electricity condition parameter SOC, and it is each little that statistics kth is grouped Air-conditioning quantity in interval is respectively m_k1,m_k2,…,m_ki,…,m_kN, by the state-of-charge parameter of the air-conditioning in i-th minizone Unified for SOC_i:

${\mathrm{SOC}}_i=\frac{1}{N}i-\frac{1}{2N}---\left(23\right)$

Calculate the air-conditioned maximum rise general power of the institute in the i-th minizone of kth packetLower with maximum General powerIt is respectively as follows:

$P_{max,ki}^{up}=m_{ki}{P}_{\max ,k}^{up}\left({\mathrm{SOC}}_i\right)---\left(24\right)$

$P_{max,ki}^{down}=m_{ki}{P}_{\max ,k}^{down}\left({\mathrm{SOC}}_i\right)---\left(25\right)$

Wherein:For kth packet i-th minizone in each air-conditioning maximum on adjusting power,For adjusting power under the maximum of each air-conditioning in the i-th minizone of kth packet；

The maximum of whole air-conditioning group raises general powerGeneral power is lowered with maximumIt is respectively as follows:

$P_{\max \_total}^{up}-=\underset{k=1}{\overset{K}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}^{up}---\left(26\right)$

$P_{\max \_total}^{down}=\underset{k=1}{\overset{K}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{max,ki}^{down}---\left(27\right)$

Wherein:Maximum for whole air-conditioning group raises general power,Maximum downward for whole air-conditioning group General power.

Step 4: set up the virtual robot arm model of air-conditioning group, according to the operation characteristic of virtual plant, builds virtual robot arm and adjusts Degree cost.

Before not calling virtual robot arm, the load income of electricity charge F that virtual plant obtains^ahFor:

F^ah=λ_aP_L (28)

After calling virtual robot arm, the load income of electricity charge F that virtual plant obtains^afFor:

F^af=λ_a(P_L+P_G) (29)

Virtual plant is to the reimbursement for expenses F of virtual robot arm₁ ^VGFor:

$F_1^{VG}=\left\{\begin{array}{cc}{\mathrm{\&lambda;}}_b{P}_G& P_G\&GreaterEqual;0\\ -{\mathrm{\&lambda;}}_b{P}_G& P_G<0\end{array}\right.---\left(30\right)$

Income difference before and after virtual plant schedule virtual unit is defined as the scheduling cost F into virtual robot arm^VG:

F^VG=F^ah-(F^af-F₁ ^VG) (31)

Calculate the unit scheduling cost λ of virtual robot arm_vFor:

${\mathrm{\&lambda;}}_v=\frac{F^{VG}}{\left|P_G\right|}={\mathrm{\&lambda;}}_b-{\mathrm{\&lambda;}}_a\frac{P_G}{\left|P_G\right|}---\left(32\right)$

Wherein: λ_vUnit for virtual robot arm dispatches cost, λ_bThe load compensation unit price of user is paid for virtual robot arm, λ_aFor the electricity consumption unit price of user, P_GFor the total activation power of virtual robot arm, P_LFor loading.

Step 5: in ahead market, according to wind power prediction situation next day, with virtual plant maximizing the benefits as mesh Scalar functions, optimizes virtual robot arm next day and exerts oneself.

In ahead market, according to wind power prediction situation next day, with virtual plant maximizing the benefits as object function, Optimizing virtual robot arm next day to exert oneself, object function is:

$\max F_1=\underset{i=1}{\overset{H}{\&Sigma;}}\left(F_{VPP}\right(i)+F_L(i)-F_{RES}(i)-F_{VG}(i\left)\right)---\left(33\right)$

Wherein:

F_VPP(i)=λ_c(i)(P_WG(i)+P_G(i)-P_L(i)) (34)

F_L(i)=λ_aP_L(i) (35)

F_RES(i)=λ_aP_WG(i) (36)

F_VG(i)=λ_vP_G(i) (37)

Wherein: hop count when H is every day total；F₁For virtual plant at the total revenue of ahead market, F_VPPI () is virtual electricity Factory is at the sale of electricity income of the i-th period of ahead market, F_LI () is the virtual plant load electricity charge income in the i-th period of ahead market, F_RESI () is that virtual plant buys the cost of electricity, F in the i-th period of ahead market from wind energy turbine set_VGI () is that virtual plant is a few days ago I-th period of market pays the compensation of virtual robot arm and spends, λ_cI () is ahead market next day the i-th period cleaing price of prediction, P_WGI () is the wind power of i-th period of next day of prediction, P_LI () is the loading of i-th period of next day of prediction, P_GI () is pre- The total activation power of the i-th period of the next day virtual robot arm surveyed, P_GI () is decision variable；

Constraints is:

$-P_{\max \_total}^{down}\left(i\right)\&le;P_G\left(i\right)\&le;P_{\max \_total}^{up}\left(i\right)---\left(38\right)$

Wherein:For prediction the i-th period of next day virtual robot arm maximum under adjusting power,For Adjusting power in the maximum of the i-th period of next day virtual robot arm of prediction.

Step 6: for reducing virtual plant economic loss in Real-time markets further, according to wind power and outdoor Temperature prediction situation, exerts oneself to virtual robot arm and carries out rolling optimization, it is achieved virtual plant maximization of economic benefit.

In Real-time markets, for reducing the economy that virtual plant brings in Real-time markets due to wind-powered electricity generation uncertainty in traffic Loss, exerts oneself to virtual robot arm according to wind power and outdoor temperature prediction case and carries out rolling optimization, it is achieved virtual plant warp Ji maximizing the benefits, object function is:

$\max F_2=\underset{i=1}{\overset{H}{\&Sigma;}}(P_{VPP}^{rt}\left(i\right)-P_{VPP}^a\left(i\right)){\mathrm{\&lambda;}}^{rt}\left(i\right)---\left(39\right)$

$P_{VPP}^{rt}\left(i\right)=P_{WG}\left(i\right)+P_G\left(i\right)-P_L\left(i\right)---\left(40\right)$

${\mathrm{\&lambda;}}^{rt}\left(i\right)=\left\{\begin{array}{cc}{\mathrm{\&rho;}}^{up}{\mathrm{\&lambda;}}_c\left(i\right)& P_{VPP}^{rt}\left(i\right)\&le;P_{VPP}^a\left(i\right)\\ {\mathrm{\&rho;}}^{down}{\mathrm{\&lambda;}}_c\left(i\right)& P_{VPP}^{rt}\left(i\right)>P_{VPP}^a\left(i\right)\end{array}\right.---\left(41\right)$

$\left\{\begin{array}{c}{\mathrm{\&rho;}}^{up}\&GreaterEqual;1\\ {\mathrm{\&rho;}}^{down}<1\end{array}\right.---\left(42\right)$

Wherein: F₂For virtual plant in the total revenue of Real-time markets,It is that the i-th period virtual plant is in Real-time markets Electricity sales amount,It is the i-th period virtual plant ahead market bid amount in Real-time markets, λ^rtI () is the injustice of prediction The electricity price of weighing apparatus the i-th period of market, ρ^upFor the rise electricity price ratio in uneven market, ρ^downDownward electricity price ratio for uneven market Rate.

Step 7: after virtual robot arm receives dispatch command, takes into account user fairness and comfort level, optimizes load adjustment amount and exists Distribution in air-conditioning group.

After virtual robot arm receives dispatch command, take into account user fairness and comfort level, optimize load adjustment amount air-conditioning group In distribution:

$P_G^k=P_G\frac{\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}{\underset{k=1}{\overset{K}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}---\left(43\right)$

$P_G^{ki}=P_G^k\frac{P_{max,ki}}{\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}---\left(44\right)$

Wherein: work as P_GWhen >=0,Work as P_GDuring ＜ 0,P_GTotal activation merit for virtual robot arm Rate,Air-conditioned total activation power in being grouped for kth,For all of sky in the i-th minizone of kth packet The total activation power adjusted.

The above is only the preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art For Yuan, under the premise without departing from the principles of the invention, it is also possible to make some improvements and modifications, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (8)

1. based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with a Real-time markets Optimization Scheduling, its feature It is: comprise the steps:

(1) set up thermodynamical model and the electrical model of single air conditioner according to conservation of energy principle and operation of air conditioner characteristic, i.e. build Relation between power P and refrigerating capacity Q of air-conditioning of vertical air-conditioning；

(2) introduce state-of-charge parameter SOC of air-conditioning, set up the relation between state-of-charge parameter SOC and power adjustment, Peak power P to air-conditioning_maxValue and minimum power P_minAdjusting power in maximum under value, and the constraint of state-of-charge parameter SOCAdjusting power under computing formula and maximumComputing formula；With peak power P_max, minimum power P_min, adjusting power in maximumCalculating parameter, maximum lower adjusting powerCalculating parameter characterization single air conditioner model, i.e. with parameter characterization single air conditioner Model；

(3) with the k-means algorithm in cluster analysis, air-conditioning group is divided into a K group, each packet unified according to parameter similarity All air-conditionings in each packet are carried out polymerization modeling by the parameter of interior air-conditioning；

(4) set up the virtual robot arm model of air-conditioning group, according to the operation characteristic of virtual plant, build virtual robot arm scheduling cost；

(5) in ahead market, according to wind power prediction situation next day, with virtual plant maximizing the benefits as object function, Optimize virtual robot arm next day to exert oneself；

(6) it is to reduce virtual plant economic loss in Real-time markets further, predicts according to wind power and outdoor temperature Situation, exerts oneself to virtual robot arm and carries out rolling optimization, it is achieved virtual plant maximization of economic benefit；

(7) after virtual robot arm receives dispatch command, take into account user fairness and comfort level, optimize load adjustment amount in air-conditioning group Distribution.

The most according to claim 1 based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method, it is characterised in that: described step (1) comprises the steps:

(11) thermodynamical model of single air conditioner is set up:

$C_a\frac{{\mathrm{dT}}_{in}}{dt}=\frac{1}{R_1}(T_{out}-T_{in})+Q^{\&prime;}-Q---\left(1\right)$

Wherein: T_outFor outdoor temperature, T_inFor indoor temperature, C_aFor the equivalent thermal capacitance of air-conditioning, R₁For the equiva lent impedance of air-conditioning, Q is The refrigerating capacity of air-conditioning, Q' is the heat dissipation capacity of indoor object, and t is the time；

(12) electrical model of single air conditioner is set up:

By the relational representation of the power P of air-conditioning and frequency f of air-conditioning it is:

P=k₁f+l₁ (2)

By the relational representation of refrigerating capacity Q of air-conditioning and frequency f of air-conditioning it is:

Q=k₂f+l₂ (3)

The relation between the power P of air-conditioning and refrigerating capacity Q of air-conditioning of setting up is:

$Q=\frac{k_2}{k_1}P+\frac{k_1{l}_2-l_1{k}_2}{k_1}---\left(4\right)$

Wherein: k₁、l₁、k₂And l₂It is constant coefficient.

The most according to claim 2 based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method, it is characterised in that: described step (2) comprises the steps:

Air-conditioning is to be stored in affiliated building with the form of heat energy by electric energy, and the highest energy storage capacity of indoor temperature is the least, Indoor Temperature Spending the lowest energy storage capacity the biggest, the comfort level scope of note user is [T_min,T_max]；If indoor temperature is T_maxTime energy storage capacity be 0, then Indoor temperature is T_inTime energy storage capacity O_inFor:

O_i=C_a(T_max-T_in) (5)

The stored energy capacitance O of building is:

O=C_a(T_max-T_min) (6)

State-of-charge parameter SOC of definition air-conditioning is energy storage capacity O_inRatio with stored energy capacitance O:

$SOC=\frac{O_{in}}{O}=\frac{T_{max}-T_{in}}{T_{\max }-T_{\min }}---\left(7\right)$

Regulation and control initial time was designated as 0 moment, and Δ t is that air-conditioning regulates and controls Cycle Length；

Relation between state-of-charge parameter SOC and the power P of air-conditioning of air-conditioning is:

$SOC\left(t\right)=SOC\left(0\right)+\frac{k_2{R}_1}{k_1(T_{\max }-T_{\min })}\left(1-e^{-\frac{\mathrm{\&Delta;}t}{R_1{C}_a}}\right)\left(P\right(t)-P(0\left)\right)---\left(8\right)$

Adjusting power in maximumComputing formula is:

$P_{\max }^{up}=\min \left(\mathrm{\&kappa;}\right(1-SOC\left(0\right)),P_{max}-\mathrm{\&delta;}SOC(0)+\mathrm{\&chi;}-{\mathrm{\&xi;T}}_{out})---\left(9\right)$

Maximum lower adjusting powerComputing formula is:

$P_{max}^{down}=\min \left(\mathrm{\&kappa;}SOC\right(0),\mathrm{\&delta;}SOC(0)-\mathrm{\&chi;}+{\mathrm{\&xi;T}}_{out}-P_{\min })---\left(10\right)$

Wherein:

$\mathrm{\&kappa;}=\frac{k_1(T_{max}-T_{\min })}{R_1{k}_2(1-e^{-\frac{\mathrm{\&Delta;}t}{R_1{C}_a}})}---\left(11\right)$

$\mathrm{\&xi;}=\frac{k_1}{R_1{k}_2}---\left(12\right)$

$\mathrm{\&chi;}=\frac{k_1{l}_2{R}_1-k_2{l}_1{R}_1-k_1{Q}^{\&prime;}{R}_1+k_1{T}_{\max }}{R_1{k}_2}---\left(13\right)$

$\mathrm{\&delta;}=\frac{k_1(T_{max}-T_{\min })}{R_1{k}_2}---\left(14\right)$

Wherein: SOC (0) and SOC (t) is respectively 0 moment and the state-of-charge parameter of t air-conditioning, P (0) and P (t) is respectively 0 Moment and the power of t air-conditioning；With parameter sets { κ ξ χ δ P_max P_minCharacterize an air-conditioning.

The most according to claim 3 based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method, it is characterised in that: described step (3) comprises the steps:

First, with the k-means algorithm in cluster analysis, air-conditioning group is divided into a K group, each point unified according to parameter similarity The parameter of air-conditioning in group, in kth packet, all air-conditionings all use { κ_k ξ_k χ_k δ_k P_max,_k P_min,_kRepresent；

Then, the excursion [0,1] of state-of-charge parameter SOC is divided into N number of minizone, according to the charged shape of every air-conditioning All air-conditionings in each packet are divided in each minizone by state parameter SOC, each minizone of statistics kth packet Interior air-conditioning quantity is respectively m_k1,m_k2,…,m_ki,,m_kN, by the state-of-charge parameter unification of the air-conditioning in i-th minizone it is SOC_i:

${\mathrm{SOC}}_i=\frac{1}{N}i-\frac{1}{2N}---\left(15\right)$

Calculate the air-conditioned maximum rise general power of the institute in the i-th minizone of kth packetTotal work is lowered with maximum RateIt is respectively as follows:

$P_{max,ki}^{up}=m_{ki}{P}_{\max ,k}^{up}\left({\mathrm{SOC}}_i\right)---\left(16\right)$

$P_{max,ki}^{down}=m_{ki}{P}_{max,k}^{down}\left({\mathrm{SOC}}_i\right)---\left(17\right)$

Wherein:For kth packet i-th minizone in each air-conditioning maximum on adjusting power,For adjusting power under the maximum of each air-conditioning in the i-th minizone of kth packet；

The maximum of whole air-conditioning group raises general powerGeneral power is lowered with maximumIt is respectively as follows:

$P_{\max \_total}^{up}=\underset{k=1}{\overset{K}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}^{up}---\left(18\right)$

$P_{\max \_total}^{down}=\underset{k=1}{\overset{K}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{max,ki}^{down}---\left(19\right)$

Wherein:Maximum for whole air-conditioning group raises general power,Maximum for whole air-conditioning group lowers total work Rate.

The most according to claim 4 based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method, it is characterised in that: calculate the unit scheduling cost λ of virtual robot arm_vFor:

${\mathrm{\&lambda;}}_v={\mathrm{\&lambda;}}_b-{\mathrm{\&lambda;}}_a\frac{P_G}{\left|P_G\right|}---\left(20\right)$

Wherein: λ_vUnit for virtual robot arm dispatches cost, λ_bThe load compensation unit price of user, λ is paid for virtual robot arm_aFor The electricity consumption unit price of user, P_GTotal activation power for virtual robot arm.

The most according to claim 5 based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method, it is characterised in that: described step (5) comprises the steps:

In ahead market, according to wind power prediction situation next day, with virtual plant maximizing the benefits as object function, optimize Next day, virtual robot arm was exerted oneself, and object function is:

$\max F_1=\underset{i=1}{\overset{H}{\&Sigma;}}\left(F_{VPP}\right(i)+F_L(i)-F_{RES}(i)-F_{VG}(i\left)\right)---\left(21\right)$

Wherein:

F_VPP(i)=λ_c(i)(P_WG(i)+P_G(i)-P_L(i)) (22)

F_L(i)=λ_aP_L(i) (23)

F_RES(i)=λ_aP_WG(i) (24)

F_VG(i)=λ_vP_G(i) (25)

Wherein: hop count when H is every day total；F₁For virtual plant at the total revenue of ahead market, F_VPPI () is that virtual plant is in day The sale of electricity income of the i-th period of front market, F_LI () is the virtual plant load electricity charge income in the i-th period of ahead market, F_RES(i) The cost of electricity, F is bought in the i-th period of ahead market from wind energy turbine set for virtual plant_VGI () is that virtual plant is at ahead market The i period pays the compensation of virtual robot arm and spends, λ_cI () is ahead market next day the i-th period cleaing price of prediction, P_WG(i) For the wind power of i-th period of next day of prediction, P_LI () is the loading of i-th period of next day of prediction, P_GI () is secondary for predict The total activation power of day the i-th period virtual robot arm, P_GI () is decision variable；

Constraints is:

$-P_{\max \_total}^{down}\left(i\right)\&le;P_G\left(i\right)\&le;P_{\max \_total}^{up}\left(i\right)---\left(26\right)$

Wherein:For prediction the i-th period of next day virtual robot arm maximum under adjusting power,For prediction Adjusting power in the maximum of the i-th period of next day virtual robot arm.

The most according to claim 6 based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method, it is characterised in that: described step (6) comprises the steps:

In Real-time markets, for reducing the economic damage that virtual plant brings in Real-time markets due to wind-powered electricity generation uncertainty in traffic Losing, exerting oneself virtual robot arm according to wind power and outdoor temperature prediction case carries out rolling optimization, it is achieved virtual plant economy Maximizing the benefits, object function is:

$\max F_2=\underset{i=1}{\overset{H}{\&Sigma;}}\left(P_{VPP}^{rt}\right(i)-P_{VPP}^a(i\left)\right){\mathrm{\&lambda;}}^{rt}\left(i\right)---\left(27\right)$

$P_{VPP}^{rt}\left(i\right)=P_{WG}\left(i\right)+P_G\left(i\right)-P_L\left(i\right)---\left(28\right)$

${\mathrm{\&lambda;}}^{rt}\left(i\right)=\left\{\begin{array}{cc}{\mathrm{\&rho;}}^{up}{\mathrm{\&lambda;}}_c\left(i\right)& P_{VPP}^{rt}\left(i\right)\&le;P_{VPP}^a\left(i\right)\\ {\mathrm{\&rho;}}^{down}{\mathrm{\&lambda;}}_c\left(i\right)& P_{VPP}^{rt}\left(i\right)>P_{VPP}^a\left(i\right)\end{array}\right.---\left(29\right)$

$\left\{\begin{array}{c}{\mathrm{\&rho;}}^{up}\&GreaterEqual;1\\ {\mathrm{\&rho;}}^{down}<1\end{array}\right.---\left(30\right)$

Wherein: F₂For virtual plant in the total revenue of Real-time markets,It is the i-th period virtual plant selling in Real-time markets Electricity,It is the i-th period virtual plant ahead market bid amount in Real-time markets, λ^rtI () is the uneven city of prediction The electricity price of the i-th period of field, ρ^upFor the rise electricity price ratio in uneven market, ρ^downDownward electricity price ratio for uneven market.

The most according to claim 7 based on convertible frequency air-conditioner virtual robot arm modeling virtual plant a few days ago with Real-time markets optimization Dispatching method, it is characterised in that: described step (7) comprises the steps:

After virtual robot arm receives dispatch command, take into account user fairness and comfort level, optimize load adjustment amount in air-conditioning group Distribution:

$P_G^k=P_G\frac{\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{max,ki}}{\underset{k=1}{\overset{K}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}---\left(31\right)$

$P_G^{ki}=P_G^k\frac{P_{max,ki}}{\underset{i=1}{\overset{N}{\&Sigma;}}{P}_{\max ,ki}}---\left(32\right)$

Wherein: work as P_GWhen >=0,Work as P_GDuring ＜ 0,P_GFor the total activation power of virtual robot arm, Air-conditioned total activation power in being grouped for kth,For all of air-conditioning total in the i-th minizone of kth packet Schedule power.