CN109739093A - A kind of resident's electric appliance mixing control method based on PMV model - Google Patents
A kind of resident's electric appliance mixing control method based on PMV model Download PDFInfo
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Abstract
The invention discloses a kind of resident's electric appliance mixing control method based on PMV model, obtains current hot comfort situation including measuring and estimating the parameter for calculating PMV model, such as current indoor temperature, relative humidity, wind speed, and by PMV model；According to Spot Price information, the pmv value of balanced network load and hot comfort is calculated；Using modified particle swarm optiziation, optimal room temperature setting value is reversely solved based on PMV model；According to calculated desired temperature, room temperature is adjusted to relevant temperature by electrical appliance.The present invention can make residential electricity consumption device according to Spot Price information, in the power grid peak of power consumption period by suitably reducing comfort level, that is, electrical appliance power is reduced, to reduce power grid peak load, to realize under conditions of guaranteeing each household residential electricity consumption comfort level, electric cost and energy consumption are reduced.
Description
Technical field
The invention belongs to smart grids and airconditioning control field, and in particular to a kind of resident's electric appliance based on PMV model is mixed
Combination control method.
Background technique
With being continuously increased for resident's air-conditioning and electric heating plant capacity and quantity, power demand sharp increase, make resident this
The influence of part electric appliance Load on Electric Power Grid electricity consumption peak value is increasing.Such as in summer continuous high temperature weather, air-conditioning temperature-reducing conduct
It is main to use electric consumption, network load will be made persistently to rise in the large-scale use of same period, be brought seriously to power grid security
Hidden danger.To avoid electric network fault caused by excess load, power supply company can only sequentially ration the power supply at present, thus to the life for the resident that rationed the power supply
Work causes very big inconvenience.However, according to Spot Price information, the mode that resident can respond as desired is used in power grid
Electric peak period by suitably reducing comfort level, that is, reduces electrical appliance power, to reduce power grid peak load, to protect in turn
Demonstrate,prove the electricity consumption comfort level of each household resident.
Traditional room temperature is when being adjusted, although can control in set temperature, do not account for it is other because
Element, other than room temperature, indoor thermal comfort is also by relative humidity, wind speed, radiation temperature, the clothes thermal resistance of resident and human body
Metabolic rate influence.
PMV (Predicted Mean Vote) is an index of current internationally recognized description indoor thermal environment, can be with
Than the hot comfort of more objective reflection indoor thermal environment.The prediction result of PMV model and the perception of human body be it is almost the same,
Therefore the air conditioning control method based on hot comfort also achieves good effect.But at present for resident's hot comfort
Research only considers to optimize electric control method to improve comfort level, for utilizing PMV model realization active balance hot comfort
Research with network load is not still comprehensive.
Summary of the invention
In view of the deficienciess of the prior art, the purpose of the present invention is to propose to a kind of resident's electric appliances based on PMV model to mix
Combination control method, to realize active balance and optimization resident's hot comfort and network load.
A kind of resident's electric appliance mixing control method based on PMV model, comprising the following steps:
Step 1, the data of PMV Model Parameter are obtained；
Step 2, hot comfort is calculated according to the Spot Price y (t) in the t timeValue:
In summer, have:
When in winter, have:
Wherein, σ ∈ [4,10] and α ∈ [0.5,1] indicates user's custom parameter；
Step 3, the hot comfort that will be calculatedValue be updated in PMV model, utilize the population optimized
Algorithm solves indoor Optimal Temperature；
Step 4, household electrical appliance are adjusted according to obtained indoor Optimal Temperature.
Further, the PMV model are as follows:
PMV=[0.303exp (- 0.036M)+0.028] { (M-W)
-3.05·10-3·[5733-6.99·(M-W)-pa]
-0.42·[(M-W)-58.15]-1.7·10-5·M·(5867-pa)
-0.0014·M·(34-Tair)-3.96·10-8·fcl·[(Tcl+273)4
-(TMRT+273)4]-fcl·hc·(Tcl-Tair)}
Wherein:
Tcl=35.7-0.028 (M-W)-Icl·{3.96·10-8·fcl·[(Tcl+273)4-(TMRT+273)4]+
fcl·hc·(Tcl-Tair)}
M indicates that unit area human metabolism leads, unit: W/m2；W indicates unit area human body power, unit: W/
m2；paFor steam partial pressure in room air, unit: Pa；TairIndicate indoor environment temperature, unit: DEG C；fclIndicate human body
It wears the clothes and nude surface area ratio, TclIndicate garment surface temperature, unit: DEG C；TMRTIndicate Average indoor radiation temperature, unit:
℃；hcFor heat exchange coefficient, unit: W/ (m2·K)；vairIndicate room air flow velocity, unit: m/s；IclIndicate clothes
Thermal resistance, unit: m2·K/W。
Further, going out indoor Optimal Temperature using the PSO Algorithm optimized described in step 3 includes:
3.1, m particle is generated, using room temperature setting value as the position of particleWherein k is the number of iterations, and i is
Particle serial number i ∈ 1,2 ..., m }；
3.2, generate the initial position of particleIt calculatesFitness valueCompare all particles
Fitness valueSize updatesIndividual optimal valueAnd global optimum
Wherein abs function representation takes absolute value,For by the position of particleAs
Simplifying in the PMV model that indoor temperature value is brought into indicates；
3.3, by updated individual optimal valueAnd global optimumIt brings into:
With
The speed of more new particle and position, whereinIt is the speed of i-th of particle kth time iteration, w is inertia weight, c1、
c2It is Studying factors, rand () is the random number between (0,1)；
3.4, if the number of iterations k reaches the number of setting, export global optimumI.e. indoor Optimal TemperatureIf
The number of iterations k is not up to the number set, then the number of iterations is set as k+1, and restarts to execute step 3.2.
4. according to claim 1 with resident's electric appliance mixing control method described in 3 based on PMV model, which is characterized in that
The initial position of the generation particleIncluding using traditional random device to generate the initial position and benefit of particle
With
Optimization generates the initial position of particle, whereinThe room temperature obtained for the last operation particle swarm algorithm
Optimal value, γ are search radius, and rand () is the random number between (0,1)；
Compared with prior art, the beneficial effects of the present invention are:
(1) setting value at room temperature of balanced hot comfort and network load has been found out by PMV model, thus guaranteeing human body
Network load is reduced under the premise of hot comfort, to achieve the purpose that economy, safety, energy conservation, emission reduction.
(2) PSO algorithm is optimized, so that effect is more preferable when solving indoor Optimal Temperature.
Detailed description of the invention
Fig. 1 is the work flow diagram of Intelligent Hybrid Control method of the present invention；
Fig. 2 is PMV model and scale schematic diagram；
Fig. 3 is that Optimal Temperature setting value solves flow chart；
Fig. 4 is that whether there is or not the experimental data curve figures of room temperature setting value and pmv value under load balancing for comparison.
Fig. 5 is that whether there is or not the experimental data curve figures of the power grid total load under load balancing for comparison.
Specific embodiment
As shown in Figure 1, a kind of resident's electric appliance mixing control method based on PMV model, comprising the following steps:
Step 1, the data of PMV Model Parameter are obtained；
PMV (Predicted Mean Vote) predicts average ratings, and index is with the thermally equilibrated fundamental equation of human body
And the grade of psychophysiology Subjective Thermal Feeling is starting point, it is contemplated that many commenting comprehensively in relation to factor of human thermal comfort sense
Valence index, for range between [- 3,3], pmv value indicates hot comfort by being as cold as heat from small to large.
The PMV model are as follows:
Wherein
Formula (1) can be simplified shown as:
PMV=F (Tair,TMRT,vair,hc,Icl,M) (5)
M indicates that unit area human metabolism leads, unit: W/m2, indicate unit area human body power, typical motion
Metabolic rate is as shown in table 1；
paFor steam partial pressure in room air, unit: Pa is set as 0.1MPa；
TairIndicate indoor environment temperature, unit: DEG C, general room temperature is between 16 DEG C~30 DEG C；
fclIndicate that human body is worn the clothes and nude surface area ratio, the ratio between indoor clothing amount is set as 1.15 in winter, in summer
The ratio between indoor clothing amount is set as 1.1；
TclIndicate garment surface temperature, unit: DEG C；
TMRTIndicate Average indoor radiation temperature, unit: DEG C, it is set as 24 DEG C；
hcFor heat exchange coefficient, unit: W/ (m2·K)；
vairIndicate room air flow velocity, unit: m/s is set as 0.1m/s；
IclIndicate clothes thermal resistance, unit: m2K/W, typical clothing thermal resistance are as shown in table 2.
1 typical motion metabolic rate (1met=58.1W/m of table2)
The typical clothing thermal resistance of table 2
Step 2, hot comfort is calculated according to the Spot Price y (t) in the t timeValue:
The electricity price generated in the present embodiment in view of different due to the time is different, therefore in the t time, it obtains local real
When electricity price information be y (t), the range of Spot Price y (t) generally between [0,2], establishes Spot Price y (t) and hot comfort
ValueCorrelation function:
In summer, have:
When in winter, have:
Wherein, σ and α indicates user's custom parameter, and resident can define different hot comfort ranges and carry out personalization
Setting, wherein σ ∈ [4,10] is function amplitude, and α ∈ [0.5,1] indicates the range of user's acceptable pmv value in summer
For [0, α], in winter when, user's acceptable pmv value is [- α, 0].In the present embodiment, user is set in summer thermal comfort
The range of degree is between [0,0.8], when winter between [- 0.8,0].
Step 3, the hot comfort that will be calculatedValue be updated in PMV model, utilize the population optimized
Algorithm solves indoor Optimal Temperature；
Here by the simplified formula of PMV (5), inverse function G of the function F about room temperature is found out, then reality can be passed through
When electricity price y (t) when optimal pmv value (the i.e. PMV that finds out*) when optimal room temperatureIt indicates are as follows:
However, being iterated to calculate involved in the calculating process of pmv value, inverse function expression formula G can not be directly found and therefore adopted
With modified particle swarm optiziation (PSO), the Optimal Temperature under optimal thermal comfort value is reversely solved based on PMV modelSpecifically
Including the following steps:
3.1, m=50 is set in the present embodiment, that is, 50 particles are generated, using room temperature setting value as the position of particle
It setsWherein k is the number of iterations, and i is particle serial number, i=1,2,3 ... 50, and range is
3.2, generate the initial position of particleIn resident's household electrical appliance environment, in the different periods
Indoor Optimal Temperature is different, so needing repeatedly to carry out solving indoor Optimal Temperature, solves indoor optimal temperature in first time
When spending, particle is initialized using traditional random generation method, reruning later, PSO Algorithm is new
When indoor Optimal Temperature, initialized according to position of the following formula to particle:
WhereinFor the room temperature optimal value that the last operation particle swarm algorithm obtains, i.e., the last operation is originally
The obtained indoor Optimal Temperature of method, γ are search radius, the present embodiment 3, rand () be between (0,1) with
Machine number.
Calculate the position of all particlesFitness valueCompare fitness value size, works as fitness valueMore hour, thenIt is more excellent, show that individual optimal value and global optimum, individual optimal value refer to that single particle is looked for
The history optimal solution arrived, global optimum refer to the optimal solution that entire population is found at present:
Wherein abs function representation takes absolute value,For by the position of particleAs
Simplifying in the PMV model that indoor temperature value is brought into indicates；
3.3, particle rapidity is updated according to formula (11), whereinIt is the speed of i-th of particle kth time iteration, w is inertia
Weight, c1、c2It is Studying factors, rand () is the random number between (0,1), w=0.72, c in the present embodiment1=c2=
0.49, according to the position of formula (12) more new particle；
3.4, if the number of iterations k reaches the number of setting, export global optimumI.e. indoor Optimal TemperatureIf
The number of iterations k is not up to the number set, then the number of iterations is set as k+1, and restarts to execute step 3.2.
Step 4, household electrical appliance are adjusted according to obtained indoor Optimal Temperature.
The Optimal Temperature under optimal thermal comfort value has been acquired by step 3Again using this temperature value as Indoor Temperature
Setting value is spent, room temperature is adjusted so far setting value by electrical appliance.
The present embodiment calculates electrical appliance power Q by existing pid algorithmp(t):
Wherein,Kp、Ki、KdRespectively proportionality coefficient, integration time constant and derivative time constant,
Its value is Kp=2000, Ki=0.5, Kd=0.Electrical appliance power Q is adjusted by pid algorithmp(t), so that room temperature is adjusted to most
Excellent room temperature setting valueThis temperature can guarantee under the conditions of human thermal comfort degree, reduce network load.
Effect of the invention is described further below with reference to experiment and experimental result attached drawing:
What two width subgraphs indicated above Fig. 4 is that resident is set using the temperature of electric heating in the case where not accounting for balanced network load
The variation of value and indoor pmv value.As can be seen that the value of PMV maintains always in [- 0.5,0.5] range, indicate that user has most preferably
Comfort level.But a large number of users seeks that comfort level is optimal, can aggravate the load pressure of peak of power consumption period power grid.Two width of lower section
The variation for considering desired temperature and pmv value under network load is shown in subgraph.It can be known by red Spot Price curve
Road 18:00~22:00 (i.e. 64800s~79200s) be high electricity price, peak of power consumption period, can be right using method of the invention
Electric heating equipment is adjusted, and by reducing electric heating plant capacity, reduces power grid under conditions of not excessive influence users'comfort
Load.From lower-left, figure be can see, and in the peak of power consumption period, pmv value indicates that temperature pleasant degree exists between [- 0.5, -0.8]
It is comfortable and it is micro- it is cool between, be user's acceptable.When participating in demand response, more using the number of users of the method for the present invention
When, power grid peak load can be effectively reduced, as shown in figure 5, only electric heating is a kind of when 3000 family families use the method for the present invention
Electric appliance can be in the load of electricity consumption peak period reduction about 500kw.
Claims (4)
1. a kind of resident's electric appliance mixing control method based on PMV model, which comprises the following steps:
Step 1, the data of PMV Model Parameter are obtained；
Step 2, hot comfort is calculated according to the Spot Price y (t) in the t timeValue:
In summer, have:
When in winter, have:
Wherein, σ ∈ [4,10] and α ∈ [0.5,1] indicates user's custom parameter；
Step 3, the hot comfort that will be calculatedValue be updated in PMV model, utilize the particle swarm algorithm optimized
Solve indoor Optimal Temperature；
Step 4, household electrical appliance are adjusted according to obtained indoor Optimal Temperature.
2. resident's electric appliance mixing control method according to claim 1 based on PMV model, which is characterized in that described
PMV model are as follows:
PMV=[0.303exp (- 0.036M)+0.028] { (M-W)
-3.05·10-3·[5733-6.99·(M-W)-pa]
-0.42·[(M-W)-58.15]-1.7·10-5·M·(5867-pa)
-0.0014·M·(34-Tair)-3.96·10-8·fcl·[(Tcl+273)4
-(TMRT+273)4]-fcl·hc·(Tcl-Tair)}
Wherein:
Tcl=35.7-0.028 (M-W)-Icl·{3.96·10-8·fcl·[(Tcl+273)4
-(TMRT+273)4]+fcl·hc·(Tcl-Tair)}
M indicates that unit area human metabolism leads, unit: W/m2；W indicates unit area human body power, unit: W/m2；pa
For steam partial pressure in room air, unit: Pa；TairIndicate indoor environment temperature, unit: DEG C；fclIndicate human body wear the clothes with
Nude surface area ratio, TclIndicate garment surface temperature, unit: DEG C；TMRTIndicate Average indoor radiation temperature, unit: DEG C；hc
For heat exchange coefficient, unit: W/ (m2·K)；vairIndicate room air flow velocity, unit: m/s；IclIndicate clothes thermal resistance,
Unit: m2·K/W。
3. resident's electric appliance mixing control method according to claim 1 based on PMV model, which is characterized in that step 3 institute
The PSO Algorithm that the utilization stated optimized goes out indoor Optimal Temperature, comprising:
3.1, m particle is generated, using room temperature setting value as the position of particleWherein k is the number of iterations, and i is particle
Serial number i ∈ 1,2 ..., m }；
3.2, generate the initial position of particleIt calculatesFitness valueCompare the adaptation of all particles
Angle valueSize updatesIndividual optimal valueAnd global optimum
Wherein abs function representation takes absolute value,For by the position of particleAs interior
Simplifying in the PMV model that temperature value is brought into indicates；
3.3, by updated individual optimal valueAnd global optimumIt brings into:
With
The speed of more new particle and position, whereinIt is the speed of i-th of particle kth time iteration, w is inertia weight, c1、c2It is
Studying factors, rand () are the random numbers between (0,1)；
3.4, if the number of iterations k reaches the number of setting, export global optimumI.e. indoor Optimal TemperatureIf iteration
Number k is not up to the number set, then the number of iterations is set as k+1, and restarts to execute step 3.2.
4. according to claim 1 with resident's electric appliance mixing control method described in 3 based on PMV model, which is characterized in that it is described
Generation particle initial positionIncluding using traditional random device to generate initial position and the utilization of particle
Optimization generates the initial position of particle, whereinThe room temperature obtained for the last operation particle swarm algorithm is optimal
Value, γ are search radius, and rand () is the random number between (0,1).
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Application publication date: 20190510 |