CN108764524A - A kind of rolling optimal dispatching method of household energy management system - Google Patents
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Abstract
The invention discloses a kind of rolling optimal dispatching methods of household energy management system, including：1) according to domestic consumer's electrical equipment model and demand response mechanism model, the in a few days household electricity Optimized model of two benches optimization is established；2) in a few days household electricity Optimized model is solved, utilize household energy management system acquisition user power utilization behavioural information and demand response mechanism information, the uncertain variables predicted value of comprehensive present period, input in a few days in household electricity Optimized model, are solved by solver；3) optimal value of k+1 period electrical equipment variables is executed；Execute real-time electricity consumption adjustable strategies, the whole load of adjustment power adjustable, energy storage power；4) reach k period Mos when real-time electricity consumption adjusts, using the k+1 periods as new original state, return to step 2).The present invention solves the problems, such as equipment real-time control in rolling optimization interval, and reduce forecasting inaccuracy influences caused by user's benefit, has handled demand response mechanism to customer impact.
Description
Technical field
The present invention relates to intelligent power field more particularly to a kind of rolling optimal dispatching sides of household energy management system
Method.
Background technology
Important link of the intelligent power as hair transmission & distribution change, is the key point of power grid intelligent Service.By operating a switch
The conventional requirement side Managed Solution rationed the power supply no longer has met user under the new situation wants reliability, intelligence, judicial convenience electricity consumption
It asks.With the continuous development of intelligent grid, automatic intelligent control system and user's two-way interactive technology are promoted in user side
It is possibly realized, efficient between structure power grid and client, interactive is novel for electricity consumption relationship, and diversification interaction is provided for power customer
Service is that current intelligent power develops the new demand faced.Meanwhile with the continuous improvement of household electrical appliances intelligence degree, resident
Reasonable arrangement and optimization are carried out to reduce electricity cost to home intelligent power behavior using household energy management system.Pass through confession
Need the information interaction of both sides, user that can understand the real-time dynamic of power grid in time, it is reasonable while participating in demand response project
Arrangement power program and response policy.
User's two-way interactive technology allows user to participate in real time in response project.Current needs response mechanism can be divided into valence
The research object of lattice demand response and stimulable type demand response, most of stimulable type demand response is industry and commerce user, to family
It is less that front yard user participates in the research of stimulable type demand response.Meanwhile such as distributed photovoltaic, the novel hair electric equipment of energy storage in family
Middle application is gradually extensive, and domestic consumer will face more enchancement factors, and local environment will become more complicated.Study new environment
Under household energy management system Optimization Scheduling have certain necessity.
Currently, to the research of the household energy management system Optimization Scheduling under new environment, there is also certain deficiencies.It is early
The literature research in the phase field focuses mostly in optimization or pure real-time control a few days ago.The advantage optimized a few days ago is to hold
Global information, it is easy to accomplish global optimum, but increasing with uncertain factor, a few days ago effect of optimization will be deteriorated, especially
Optimization is more difficult to competent a few days ago after the case where considering demand side management；Real-time control can be according to local message Real-time Decision
The controlled quentity controlled variable of current device, uncertain factor it is influenced it is smaller, but due to using local message, it is difficult to reach it is global most
It is excellent.
Recent part document introduces the Rolling optimal strategy based on Model Predictive Control, can preferably coping with uncertainty
Factor, but the equipment control situation in each rolling time interval is not studied.
Invention content
The present invention provides a kind of rolling optimal dispatching method of household energy management system, the present invention improves user's ginseng
It with the enthusiasm of demand response, will in a few days rolling optimization be combined with real-time control, while holding global optimum, solve
In rolling optimization interval the problem of equipment real-time control, reducing is influenced due to forecasting inaccuracy caused by user's benefit, very well
Handled influence of the demand response mechanism to user, it is described below：
A kind of rolling optimal dispatching method of household energy management system, the described method comprises the following steps：
1) according to domestic consumer's electrical equipment model and demand response mechanism model, the in a few days family of two benches optimization is established
Electricity consumption Optimized model；
2) in a few days household electricity Optimized model is solved, user power utilization behavior is acquired using household energy management system
Information and demand response mechanism information, the uncertain variables predicted value of comprehensive present period, in a few days household electricity optimizes mould for input
In type, solved by solver；
3) optimal value of k+1 period electrical equipment variables is executed；Real-time electricity consumption adjustable strategies are executed, adjustment power adjustable is whole
Load, energy storage power；
4) reach k period Mos when real-time electricity consumption adjusts, using the k+1 periods as new original state, return to step 2).
Further, the demand response mechanism model includes：Price type demand response model and stimulable type demand response
Model；
Wherein, stimulable type demand response model includes maximum power constraint and demand response event Constraint.
Further, the in a few days household electricity Optimized model is specially：
The first stage Optimized model for being up to target with user's benefit includes：The object function of first stage and constraint item
Part；
Second stage Optimized model with the minimum target of power purchase power swing includes：The object function of second stage with about
Beam condition.
When specific implementation, the maximum power constraint representation is：
In formula, K={ 1,2 ..., T },Indicate the mean power of k period user power purchases, Pconst,kIndicate maximum power limit
Value processed.
Wherein,
The demand response event Constraint is expressed as：
In formula, PreFor the mean power that the period cuts down, PreAlso need to meet maximin constraint；
Response is happened in setting time section, is had continuity in time, is also needed to meet constraint：
rk=0, k≤m or k > n, rk≥rk+1,m+1≤k≤n-1。
Further,
The object function of first stage optimization：By electric cost expenditure function C1, demand response revenue function C2, comfort level income
Function C3Composition：
MinC=(1- ω) (C1-C2)-ωC3
When specific implementation, the object function of second stage optimization is expressed as：
In formula,For user's power purchase power average value in first stage optimum results.
Further, the constraints of the second stage also needs to meet while including first stage constraints：
Cto≤Ctomin
In formula, CtominFor the objective function optimization result of first stage.
It is described to execute real-time electricity consumption adjustable strategies and be specially：
1) when current out-of-limit value-at-risk is less than risk threshold value, indicate that there are out-of-limit risks, if Δ Pt>0, Δ PtSuccessively by storing
Battery, grid balance, accumulator SOC lower limits are SOCmin；If Δ Pt<0, Δ PtSuccessively by accumulator, air-conditioning, grid balance, store
Battery SOC lower limit is SOCmin+SOCbak,k, then go to step 3)；
When current out-of-limit value-at-risk is more than or equal to risk threshold value, judge whether present period electricity price is in peak period, if
Electricity price is in peak period and goes to step 2)；If no, accumulator SOC lower limits are SOCmin+SOCbak,kIf Δ Pt>0, Δ Pt
It is balanced successively by accumulator, power grid, if Δ P<0, Δ PtBy grid balance, and go to step 3)；
2) judge battery powerWhether 0 is more than, if more than then Δ PtBy grid balance, if no more than if preferentially by
Battery balance, accumulator SOC lower limits are SOC at this timemin+SOCbak,k, residual deviation is by grid balance；
3) judge the k periods, whether purchase of electricity met power limit；
Flow terminates if meeting, and completes this real-time electricity consumption adjustment；
If not satisfied, then undertaking all loads by accumulator, prompts user power utilization power excessive, need to cut down manually non-adjustable
Whole load, if accumulator SOC reaches SOC at this timemin, alert user and exceeded electricity consumption limitation, this real-time electricity consumption adjustment is complete
At.
The advantageous effect of technical solution provided by the invention is：
(1) present invention has fully considered the initiative of user, and establishing to constrain containing maximum power can make decisions on one's own with user
Demand response event (DRE) constraint Optimized model, MINLP model problem is translated into, compared to using opening
The nonlinear model that hairdo algorithm solves, solving result are easily optimal, and more efficient；
(2) this invention takes the Multiple Time Scales control strategies that in a few days rolling optimization is combined with real-time control, in handle
While holding global optimum, also solves the problems, such as equipment real-time control in rolling optimization interval, reduce due to forecasting inaccuracy
Influence, even closer with actually contacting caused by user's benefit.
Description of the drawings
Fig. 1 is the flow chart of a kind of rolling optimal dispatching method of household energy management system；
Fig. 2 is the schematic diagram of demand response event；
Fig. 3 is the flow chart of real-time electricity consumption adjustment；
Fig. 4 is the schematic diagram that time adjustable load runs power curve；
Fig. 5 is the schematic diagram of the tou power price in one day；
Fig. 6 is the schematic diagram that cannot be adjusted load, photovoltaic output, ambient temperature and emulate actual value；
Fig. 7 is the schematic diagram for predicting percentage error curve；
Fig. 8 is the schematic diagram of time adjustable load optimization electricity condition；
Fig. 9 is the schematic diagram of indoor temperature curve；
Figure 10 is the schematic diagram of battery power curve and SOC curves；
Figure 11 is the schematic diagram that the real-time electricity consumption of power purchase power adjusts simulation curve；
Figure 12 is the schematic diagram that the real-time electricity consumption of accumulator cell charging and discharging power adjusts simulation curve.
Specific implementation mode
To make the object, technical solutions and advantages of the present invention clearer, embodiment of the present invention is made below further
It is described in detail on ground.
Embodiment 1
An embodiment of the present invention provides a kind of rolling optimal dispatching methods of household energy management system, referring to Fig. 1, the party
Method includes the following steps：
101：According to domestic consumer's electrical equipment model and demand response mechanism model, the in a few days family of two benches optimization is established
Front yard electricity consumption Optimized model；
102：In a few days household electricity Optimized model is solved, user power utilization row is acquired using household energy management system
For information and demand response mechanism information, the uncertain variables predicted value of comprehensive present period k, in a few days household electricity optimizes for input
In model, solved by solver；
103：Execute the optimal value of k+1 period electrical equipment variables；Real-time electricity consumption adjustable strategies are executed, power adjustable is adjusted
Whole load, energy storage power；
104：Reach k period Mos when real-time electricity consumption adjusts, using the k+1 periods as new original state, return to step 102.
In conclusion the embodiment of the present invention has fully considered the initiative of user, establishes and constrain and use containing maximum power
The Optimized model for the DRE constraints that family can make decisions on one's own, is translated into MINLP model problem, compared to using inspiration
The nonlinear model that formula algorithm solves, solving result are easily optimal, and more efficient.
Embodiment 2
The scheme in embodiment 1 is further introduced with reference to Fig. 2-Figure 11, it is described below：
201：The user power utilization that founds a family device model and demand response mechanism model；
In implementation process, user power utilization behavioural information includes：The section α of time transferable load electricity consumption timea(under
Limit) and βaThe transferable load setting temperature T of (upper limit), powerset；Demand response mechanism information begins to respond to time sDR, response when
Long upper limit bDR, response time lower limit aDR, cut down power minimum Premin, cut down power maximum value Premax。
One, domestic consumer's electrical equipment model
Wherein, domestic consumer's electrical equipment model includes：It cannot be adjusted load model, time adjustable load model, work(
Rate adjustable load model, energy storage model and distributed photovoltaic device model, establish following mathematical model：
(1) it cannot be adjusted load model and use definite value, be denoted asIndicate that the k periods cannot be adjusted the electric power of load, word
Symbol F only plays mark action.
Wherein, specific value according in practical application set, and the embodiment of the present invention is without limitation.
(2) time adjustable load model indicates as follows：
In formula, αaWith βaAllow working time lower limit and the upper limit for load；ωaFor 0-1 variables, be the load same day whether work
Make, 1 indicates to allow work, and 0 indicates not work, if not working, will be impacted to users'comfort, different loads is to user
Influence degree is different；qa,k、qa,iFor 0-1 variables, starting state whether is undergone for load k periods, i periods, 0 indicates not undergo
Starting state, 1 indicates experience starting state；MaIndicate duty cycle duration；For the power that load a is consumed in the k periods,The electric power sequence of one action flow is completed for load a.
(3) the whole load model of power adjustable uses air-conditioning model, is represented by：
In formula,Indicate k period indoor temperatures；Indicate that mean temperature outside the period rooms k, outdoor temperature are pre- by weather
It measures out, ε is temperature inertness coefficient, is one and the relevant amounts of Δ k, and Δ k indicates that optimization time interval, η are that the heat of air-conditioning passes
Efficiency is led, A is the coefficient of heat conduction,For air-conditioning consumption electric energy,For the mean power of air-conditioning k periods.
Air-conditioning power is no more than maximum operation power, and in one day, indoor temperature is likely lower than air-conditioner temperature lower limit, but
Air-conditioning introduces 0-1 variables without heating to indicate such caseIndicate whether k periods air-conditioning works, only in work feelings
Meet the constraint of temperature bound under condition, i.e.,：
In formula, ωHVACIndicate air-conditioning work situation, 1 indicates refrigeration, and 0 indicates not freeze,Work(is run for air-conditioning maximum
Rate, Tmin、TmaxIndicate air-conditioner temperature adjustable extent bound.
(4) energy storage model is represented by：
SOCmax≥SOCk≥SOCmin+SOCbak,k (10)
μk,chPchmax≥Pk,ch≥0 (11)
μk,dischPdischmax≥Pk,disch≥0 (12)
μk,ch+μk,disch=1 (13)
In formula, V is accumulator capacity；Pk,ch、Pk,dischFor charging, discharge power, Pchmax、PdischmaxFor maximum charge,
Discharge power；SOCkIndicate the state-of-charge (state of charge, SOC) in the k periods, SOCmax、SOCminExpression allows to store
The maximum of battery, minimum state-of-charge；ηch、ηdischIndicate accumulator charging, discharging efficiency；μk,chWith μk,dischT is indicated respectively
Whether period accumulator is in charge and discharge state, is to set 1, is otherwise 0, SOCbak,kFor k period accumulator spare capacity ratios.
(5) distributed photovoltaic device model
It is generally made of polylith photovoltaic panel, peak power output can be by intensity of illumination, photovoltaic panel operating temperature, Yi Jibiao
Output power approximate representation under the conditions of standard：
In formula,GC,kRespectively k period photo-voltaic power supply peak power outputs and intensity of illumination, PSTC、GSTC、TSTCRespectively
For full test power, intensity of illumination and the reference temperature under STC standard test conditions, TC,kFor photovoltaic panel operating temperature, d is
Temperature power coefficient, Ta,kFor environment temperature.
Two, demand response mechanism model
In implementation process, demand response mechanism model includes：Price type demand response model and stimulable type demand response
Model, wherein price type demand response model uses form (known to those skilled in the art, the present invention of tou power price
Embodiment does not repeat this), stimulable type demand response model includes two kinds of constraints：Maximum power constrains and demand response event
Constraint.
Stimulable type demand response model is established, it is specific as follows：
(1) maximum power constraint representation is：
In formula, K={ 1,2 ..., T },Indicate the mean power of k period user power purchases, Pconst,kIndicate maximum power limit
Value processed.
(2) demand response event schematic diagram is shown in that Fig. 2, constraint representation are：
In formula, m=(sDR+aDR)/Δ k, n=(sDR+bDR)/Δ k, sDRTo begin to respond to time, aDR、bDRFor response time
Lower limit and the upper limit, Δ k are optimization time interval；For the load baseline mean power of user's k periods；rkFor 0-1 variables, table
Show whether the k periods respond demand response event, 1 indicates response, and 0 indicates to be not responding to；PreThe mean power cut down for the period.
P simultaneouslyreMeet maximin constraint：
Premax≥Pre≥Premin (18)
In formula, Premin、PremaxTo allow minimum, maximum reduction power.
Response only occurs in setting time section, and requires have continuity in time, also needs to meet constraint：
rk=0, k≤m or k > n (19)
rk≥rk+1,m+1≤k≤n-1 (20)
202：Establish the in a few days household electricity Optimized model of two benches optimization；
In implementation process, long duration arrangement in a few days is carried out to electricity consumption plan with electrically optimized, including：Respond the degree of DRE
And whether break period adjustable load, while to ensure economy and smooth power purchase power, establishing the day of two benches optimization
Interior household electricity Optimized model.
1) the first stage Optimized model for being up to target with user's benefit includes：Object function and constraints.
The object function C of first stage Optimized model：By electric cost expenditure function C1, demand response revenue function C2, comfort level
Revenue function C3Composition, i.e.,：
MinC=(1- ω) (C1-C2)-ωC3 (21)
In formula, ω is weight factor, and value 0-1, weight factor ω characterization users'comfort incomes are in integrally paying
Shared ratio.
Wherein, electric cost expenditure function C1It is represented by：
In formula, ρkIndicate the electricity price of k periods,Expression cannot be adjusted the average electric power of load,It is adjustable for the time
Whole load electricity consumption mean power,For battery power；Photovoltaic goes out force data and is obtained by the prediction that generates electricity.For simplified model,
Think that the sale of electricity electricity price of extra photovoltaic online is equal with purchase electricity price,Indicate that user is negative value table from power grid power purchase for positive value
Show user to power grid sale of electricity.
Demand response revenue function C2It is represented by：
In formula, cDRIndicate that unit cuts down the economic compensation of electricity.
Comfort level revenue function is by temperature pleasant degree λHVACWith electricity consumption comfort level λSComposition, is represented by：
C3=λHVAC+λS (26)
Wherein, temperature pleasant degree λHVACIt can be expressed as with quadratic function：
In formula, λHVACFor user temperature comfort level,For the relative importance of k period temperature pleasant degree, work as interior
Nobody when, can setIt is 0, avoids unnecessary electricity consumption；The purpose that negative sign is added is to make temperature pleasant degree and electricity consumption
Comfort level variation is consistent.
Electricity consumption comfort level λSIt is represented by：
In formula, γaIndicate that influence degrees of the load a to user power utilization comfort level, size are determined by user, λSIndicate user
Electricity consumption comfort level.
The Optimized model constraints of first stage includes formula (1)-(13) and formula (16)-(20).
2) include with the second stage Optimized model of the minimum target of power purchase power swing：Object function and constraints.
Second stage Optimized model object function is represented by：
In formula,For user's power purchase power average value in optimization problem (21) optimum results.
Second stage Optimized model constraints also needs full while including first stage Optimized model constraints
Foot：
Cto≤Ctomin (30)
In formula, CtominFor the objective function optimization result of first stage.
203：In a few days household electricity Optimized model solves；
In embodiment, uncertain variables predicted value includes：It cannot be adjusted predicted load Pt F, photovoltaic predicted value Pt PV, it is outdoor
Temperature prediction valueSolver is using universal algebra modeling (GAMS) software to optimization problem (21) and optimization problem
(29) it is solved, specific solution procedure is known to those skilled in the art, and the embodiment of the present invention does not repeat this.
204：Execute the optimal value of k+1 period electrical equipment variables；
In embodiment, the optimal value of k+1 period electrical equipment variables includes：Time transferable load operating conditions, power
Transferable load operation power, wherein time transferable load operating conditions are by qa,k+1It determines, works as qa,k+1When=1, which can
The transfer load k+1 periods start operation, work as qa,k+1When=0, the time transferable load k+1 periods do not start；Power is transferable
Load operation power is the operation power of k+1 period air-conditionings
205：Execute real-time electricity consumption adjustable strategies.
In embodiment, real-time electricity consumption strategy is that electric power deviation is directed on the basis of in a few days household electricity optimum results
Accumulator cell charging and discharging power is adjusted, electric power deviation is represented by：
In formula, t is to adjust the period in real time, Pt F、Pt PVLoad, the prediction of photo-voltaic power supply output power are cannot be adjusted for the t periods
Value,For corresponding actual value.
Real-time electricity consumption adjustment flow is shown in Fig. 3, is specifically expressed as follows：
(1) current out-of-limit value-at-risk E is obtained according to formula (32), note risk threshold value is ε, and risk threshold value ε is to characterize use
Family is to reduce uncertain factor to fluctuate the conservative for influencing accumulator；
As E < ε, indicate that there are out-of-limit risks, if Δ Pt>0, Δ PtSuccessively by accumulator, grid balance, at this time electric power storage
Pond SOC lower limits are SOCmin；If Δ Pt<0, Δ PtSuccessively by accumulator, air-conditioning, grid balance, accumulator SOC lower limits are at this time
SOCmin+SOCbak,k, then go to step (3).
As E >=ε, judge whether present period electricity price is in peak period, if electricity price is in peak period and goes to step
(2)；If no, accumulator SOC lower limits are SOCmin+SOCbak,kIf Δ Pt>0, Δ PtIt is put down successively by accumulator, power grid
Weighing apparatus, if Δ P<0, Δ PtBy grid balance, and go to step (3).
(2) judge battery powerWhether 0 is more than, if more than then Δ PtBy grid balance, if no more than if preferentially by
Battery balance, accumulator SOC lower limits are SOC at this timemin+SOCbak,k, residual deviation is by grid balance.
(3) judge the k periods, whether purchase of electricity met power limit.
Wherein, this real-time electricity consumption adjustment is completed if meeting, flow terminates.
If not satisfied, then undertaking all loads by accumulator, prompts user power utilization power excessive, need to cut down manually non-adjustable
Whole load, such as：If air-conditioning is working, the operation for stopping air-conditioning being needed, if accumulator SOC reaches SOCmin, warning user is
It is limited beyond electricity consumption, this real-time electricity consumption adjustment is completed.
In conclusion the embodiment of the present invention takes the Multiple Time Scales control that in a few days rolling optimization is combined with real-time control
System strategy, while holding global optimum, also solves the problems, such as equipment real-time control in rolling optimization interval, and practical
It is even closer.
Embodiment 3
Feasibility verification is carried out to the scheme in Examples 1 and 2 with reference to specific example, it is described below：
By taking summer a certain typical household configuration of load as an example, in a few days electricity consumption optimisation strategy time interval that this method uses for
The electric power data of 15min, time adjustable load 1-5 can refer to document [1], and operation power curve is as shown in figure 4, wouldn't
Consider the addition of additional adjustable load.Day part maximum average power is set as 2kW；DRE shifts to an earlier date 2h and issues, when beginning to respond to
Between be 12h, duration 0.5h-1.5h, making up price be 0.4 yuan/kWh, minimax responding power be set as 0.2kW,
1.5kW.Tou power price in one day is as shown in Figure 5.Device emulation parameter setting is as shown in table 1.It cannot be adjusted load, photovoltaic goes out
Power, ambient temperature emulation actual value are shown in Fig. 6.Temperature pleasant degree coefficient chooses 0.05, and weight factor takes 0.5, ε to take 0.02kW.It closes
In load baseline acquisition there are many mode, the embodiment of the present invention is using the Optimal Curve at 0 moment of the same day as load baseline.
Certain the typical user's power consumption parameter of table 1
Uncontrollable load, photovoltaic are contributed, the percentage error between ambient temperature predicted value and actual value can refer to document
[2], it and is numerically centainly changed, as shown in Figure 7.Curve is meant that home energy management during rolling optimization
When system is predicted every time, the worst error between day part predicted value and actual value.Then certain period predicted value and actual value
Relationship is as follows：
In formula, Pk、Respectively predicted value and actual value, r are to obey [- 1,1] equally distributed random number, EkFor error
Percentage.
Home energy management operating expense under 2 different mode of table
In table 2, has studied and do not consider the optimisation strategy a few days ago (pattern 1) of stimulable type DR constraints, do not consider stimulable type DR about
This method of beam puies forward tactful (pattern 2), the optimisation strategy a few days ago (pattern 3) for considering stimulable type DR constraints, considers stimulable type DR
This method of constraint carries the simulation result of tactful (pattern 4).Contrastive pattern 1,2, it is known that the carried policy optimization effect of this method
Compared to optimizing a few days ago, electric cost expenditure is reduced while improving comfort level, equivalent net disbursement reduces by 16.8% ((14.68-
12.21)/14.69), effect is more preferable.
Contrastive pattern 3,4, after considering DR mechanism of restriction, although this method strategy DR incomes are relaxed not as good as optimizing a few days ago
Appropriateness is apparently higher than to be optimized a few days ago, and equivalent net disbursement reduces by 11.7% ((16.47-14.55)/16.47), and does not violate
The period of maximum power constraint.Contrastive pattern 2,4, it is known that are constrained by maximum power, the equivalent expenditure of family increases, therefore designs and close
The reward mechanism of reason is the necessary condition for attracting user to participate in response, due to the response policy of this method primary study user, therefore
No longer thoroughly discuss.
Further to test this method effect of optimization, the optimum results in the case of two kinds of comparison.Predicted value is set separately
For actual value, solves and (be referred to as " exact value optimization ") using in a few days electricity consumption Optimized model, the predicted value with consideration prediction error, and
It optimizes and (is referred to as " predicted value optimization ") using the carried strategy of this method, Comparative result is shown in Fig. 8-10, due to Multiple Time Scales
A length of 1min when energy management simulation result is translated into the curve at the intervals 15min for convenience of data comparison, and size is pair
Answer the power average value in 15min.
Fig. 8 compared the electricity consumption distribution of two kinds of scene lower time adjustable loads, and load 1-5 is not cut in, selects
The period that electricity price is low in its run time section is selected.Load 1,3 avoids DRE response periods and peak value rate period, this
Also validity of this strategy in terms of reducing the electricity charge and ensuring that user meets response constraint is demonstrated.Meanwhile it can be with by comparison
Find out, the time of running of load 3,4 is identical, is only on same electricity price level although other load times of running are different
Translation, will not cause additional effect to electricity cost.
Fig. 9 can be seen that under the strategy that this method proposes, indoor temperature tightly follows accurate optimal value, demonstrates algorithm
There is certain fluctuation in premeasuring, still there is preferable robustness.Simultaneously it can be seen that in the 16-28 periods, due to electricity
Valence is relatively low, and indoor temperature maintains near set temperature, and indoor temperature occurs again into one at the time of reaching electricity price and will increase
Step decline, this is because air-conditioning carried out air storage it is cold with power cost saving.When electricity price is higher, there has also been certain for indoor maintenance temperature
Promotion.It is promoted in 60-65 period temperature, this is because time adjustable load is run at this time, about for meet demand
Beam reduces part air-conditioning power.
By Figure 10 (a) it is found that accumulator charges as energy storage medium in low electricity price, electricity price it is higher and
DRE responds period electric discharge, played an important role in terms of power cost saving.Figure 10 show rolling optimization result with it is accurate excellent
Change value trend is almost the same；But battery power raises up near 96 periods, this is because exact value optimization does not consider time
Day electricity consumption, it is optimal to run out of all energy storage at this day end, and rolling optimization predicts future for 24 hours, it is contemplated that secondary daily
Electricity is charged again in the electricity price low period.In period 52 to the period 53, accumulator SOC reaches minimum, real time execution process
In to ensure that power is limited less than demand response, spare capacity is called, is allowed to less than 0.25, in follow-up optimization process
In, spare capacity is restored to setting value again, to ensure follow-up stable operation.
Figure 11 and Figure 12 is that real-time electricity consumption adjusts simulation curve, shows that power purchase power passes through accumulator in the electricity price high period
Electric discharge cuts down part power purchase power to reduce power purchase expense.User response DRE, duration 1h, reduction known in Figure 11
For 0.872kWh, response duration electric power is not out-of-limit；Since DRE is issued for a period of time in advance, the load being cut in can
It is transferred to other periods in advance by accumulator.Curve fluctuation is more violent between 800-1000min, is because this period is negative
The mean power used when lotus actual motion power is higher than optimization causes power grid to undertake load fluctuation, but power purchase is average in 15min
Power still meet demand response limitation.As seen from Figure 12, accumulator is the wave for balancing photovoltaic output with cannot be adjusted load
Dynamic, power curve fluctuation is violent, and this also illustrates importance of the accumulator in terms of maintaining family to stablize electricity consumption.
Bibliography
[1] Giorgio A D, Pimpinella L.An event driven Smart Home Controller
enabling consumer economic saving and automated Demand Side Management[J]
.Applied Energy, 2012,96 (8)：92-103.
[2] Sharma I, Dong J, Malikopoulos A A, et al.A modeling framework for
Optimal energy management of a residential building [J] .Energy&Buildings, 2016,
130：55-63.
It will be appreciated by those skilled in the art that attached drawing is the schematic diagram of a preferred embodiment, the embodiments of the present invention
Serial number is for illustration only, can not represent the quality of embodiment.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all the present invention spirit and
Within principle, any modification, equivalent replacement, improvement and so on should all be included in the protection scope of the present invention.
Claims (9)
1. a kind of rolling optimal dispatching method of household energy management system, which is characterized in that the described method comprises the following steps：
1) according to domestic consumer's electrical equipment model and demand response mechanism model, the in a few days household electricity of two benches optimization is established
Optimized model；
2) in a few days household electricity Optimized model is solved, user power utilization behavioural information is acquired using household energy management system
With demand response mechanism information, the uncertain variables predicted value of comprehensive present period inputs in a few days household electricity Optimized model,
It is solved by solver；
3) optimal value of k+1 period electrical equipment variables is executed；Real-time electricity consumption adjustable strategies are executed, adjustment power adjustable is whole negative
Lotus, energy storage power；
4) reach k period Mos when real-time electricity consumption adjusts, using the k+1 periods as new original state, return to step 2).
2. a kind of rolling optimal dispatching method of household energy management system according to claim 1, which is characterized in that
The demand response mechanism model includes：Price type demand response model and stimulable type demand response model；
Wherein, stimulable type demand response model includes maximum power constraint and demand response event Constraint.
3. a kind of rolling optimal dispatching method of household energy management system according to claim 1, which is characterized in that institute
Stating in a few days household electricity Optimized model is specially：
The first stage Optimized model for being up to target with user's benefit includes：The object function and constraints of first stage；
Second stage Optimized model with the minimum target of power purchase power swing includes：The object function of second stage and constraint item
Part.
4. a kind of rolling optimal dispatching method of household energy management system according to claim 2, which is characterized in that
The maximum power constraint representation is：
In formula, K={ 1,2 ..., T },Indicate the mean power of k period user power purchases, Pconst,kIndicate maximum power limitation
Value.
5. a kind of rolling optimal dispatching method of household energy management system according to claim 2, which is characterized in that
The demand response event Constraint is expressed as：
In formula, PreFor the mean power that the period cuts down, PreAlso need to meet maximin constraint；
Response is happened in setting time section, is had continuity in time, is also needed to meet constraint：
rk=0, k≤m or k > n, rk≥rk+1,m+1≤k≤n-1。
6. a kind of rolling optimal dispatching method of household energy management system according to claim 3, which is characterized in that
The object function of first stage optimization：By electric cost expenditure function C1, demand response revenue function C2, comfort level revenue function
C3Composition：
MinC=(1- ω) (C1-C2)-ωC3。
7. a kind of rolling optimal dispatching method of household energy management system according to claim 3, which is characterized in that
The object function of second stage optimization is expressed as：
In formula,For user's power purchase power average value in first stage optimum results.
8. a kind of rolling optimal dispatching method of household energy management system according to claim 3, which is characterized in that
The constraints of the second stage also needs to meet while including first stage constraints：
Cto≤Ctomin
In formula, CtominFor the objective function optimization result of first stage.
9. a kind of rolling optimal dispatching method of household energy management system according to claim 1, which is characterized in that institute
Stating the real-time electricity consumption adjustable strategies of execution is specially：
1) when current out-of-limit value-at-risk is less than risk threshold value, indicate that there are out-of-limit risks, if Δ Pt>0, Δ PtSuccessively by accumulator,
Grid balance, accumulator SOC lower limits are SOCmin；If Δ Pt<0, Δ PtSuccessively by accumulator, air-conditioning, grid balance, accumulator
SOC lower limits are SOCmin+SOCbak,k, then go to step 3)；
When current out-of-limit value-at-risk is more than or equal to risk threshold value, judge whether present period electricity price is in peak period, if electricity price
Step 2) is gone in peak period；If no, accumulator SOC lower limits are SOCmin+SOCbak,kIf Δ Pt>0, Δ PtSuccessively
It is balanced by accumulator, power grid, if Δ P<0, Δ PtBy grid balance, and go to step 3)；
2) judge battery powerWhether 0 is more than, if more than then Δ PtBy grid balance, preferentially by electric power storage if being not more than
Pond balances, and accumulator SOC lower limits are SOC at this timemin+SOCbak,k, residual deviation is by grid balance；
3) judge the k periods, whether purchase of electricity met power limit；
Flow terminates if meeting, and completes this real-time electricity consumption adjustment；
If not satisfied, then undertaking all loads by accumulator, prompt user power utilization power excessive, need to cut down manually cannot be adjusted it is negative
Lotus, if accumulator SOC reaches SOC at this timemin, alert user and exceeded electricity consumption limitation, this real-time electricity consumption adjustment is completed.
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