CN108197726A - A kind of home energy data optimization methods based on improvement evolution algorithm - Google Patents
A kind of home energy data optimization methods based on improvement evolution algorithm Download PDFInfo
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Abstract
The invention discloses a kind of based on the home energy data optimization methods for improving evolution algorithm.The optimized mathematical model for the Energy Management System that founds a family, optimized mathematical model is with the minimum target as an optimization of total electricity bill spending in one day in entire household energy management system and establishes optimization object function, while establish the constraints of optimization object function；Input the parameter that optimized mathematical model needs, the optimized mathematical model is solved using evolution algorithm is improved, optimal variable parameter is obtained, optimum control is carried out to the equipment in household energy management system according to optimal variable parameter, and obtains minimum electricity charge spending.The method of the present invention can effectively solve the mathematical model.The algorithm does not need to specific computing environment, and method for solving is more excellent, is suitble to run on common electronic products.
Description
Technical field
The present invention relates to a kind of based on the home energy data optimization methods for improving evolution algorithm, belong to home energy management
Field.
Background technology
In recent decades, energy crisis and environmental pollution cause people to the very big of energy utilization efficiency and energy saving
Concern.At present, this research is concentrated mainly on industrial circle, because industrial circle possesses larger energy requirement.It is however, near
Year, with the development of economy, the electricity needs of family field also quickly increases.Therefore, it is to the research of home energy source optimization
One vital task of intelligent grid development.The energy demand growing in face of consumption of resident, academicly proposes wired home
Energy datum optimization method, home energy data optimization methods can collect the present situation of electrical work and customer consumption in family and need
It pleads condition, realizes best home energy distribution, it is energy saving to realize, the purpose of cost is reduced, and contribute to power grid security
Operation.
The research both at home and abroad for home energy optimization has much at present, primarily directed to grinding on model and optimization method
Study carefully, however generally existing following point now：Most Optimized model is all based on mixed integer linear programming model, lacks
Practical application；Simultaneously for the model established, some documents are solved using business optimization software, some use heuristic calculation
Method solves, but they all there is some shortcomings, such as business software to need to pay, be not suitable for being applied on electronic product, and
Some heuritic approach convergence rates are slow, it is impossible to which real-time when ensureing to solve is also not suitable for running on electronic product.
Two above problem restricts the popularization of home energy data optimization methods, if it cannot be solved prevent from obtaining
To extensive use.
Invention content
With the development of new energy, all kinds of low profile photovoltaic batteries have started to be applied in resident, for background technology
The Optimized model of middle home energy source management system is not perfect and optimization method existing for it is insufficient, the present invention proposes a kind of based on changing
Into the home energy data optimization methods of evolution algorithm, this method has initially set up a kind of complicated and more to meet practical mixing whole
Number nonlinear mathematical model, and be mixed with promise breaking value repair process in solution and be combined with each other with particle swarm optimization algorithm is improved,
The mathematical model is finally acquired, and is preferably solved.Extra electric energy is fed back into power grid using user in the present invention.
The technical solution adopted by the present invention is：
1) optimized mathematical model for the Energy Management System that founds a family, optimized mathematical model is with entire home energy management system
The minimum target as an optimization of one day total electricity bill spending and optimization object function is established, while establish the pact of optimization object function in system
Beam condition, constraints are constrained including basic electric appliance, and basic electric appliance constraint includes the constraint of the electric energy equilibrium of supply and demand, the thermal energy equilibrium of supply and demand
Constraint, fuel cell constraint, accumulator constraint, air-conditioning equipment constraint and switching control device constraint；
2) input optimized mathematical model need parameter, using improve improve evolution algorithm to the optimized mathematical model into
Row solve, obtain optimal variable parameter, according to optimal variable parameter in household energy management system fuel cell, electric power storage
Pond, air-conditioning equipment, switching control device carry out optimum control, and obtain minimum electricity charge spending.
The household energy management system includes fuel cell, accumulator, air-conditioning equipment, switching control device, air-conditioning
Equipment, switching control device are all connected to the power grid being mainly made of fuel cell, accumulator, major network and photovoltaic
In, fuel cell is also connected to thermal hardware, and fuel cell provides thermal energy for thermal hardware, and natural gas connects fuel cell respectively
And thermal hardware, natural gas provide natural gas energy resource for fuel cell so that natural gas energy resource is converted into heat in fuel cell
Energy and electric energy, natural gas provide natural gas energy resource so that natural gas energy resource is converted into itself needs by thermal hardware for thermal hardware
Thermal energy.
In household energy management system, basic electric energy device, fuel cell, accumulator, major network and solar energy have been further included
Photovoltaic is powered jointly for basic electric energy device, air-conditioning equipment and switching control device, and power supply volume insufficient section is mended by major network
Fill power supply.
The expression formula of the optimization object function is：
In formula, C represents the electricity charge spending of one day；Tgrid(h) electricity price at h moment, p are representedgrid(h) h moment and major network are represented
Between exchange of electric power amount, if positive number represents user and buys electricity from power grid, negative represents user and returns to power grid；Tgas
(h) Gas Prices at h moment, p are representedgas(h) the direct thermal energy supply of the natural gas at h moment is represented；PeFC(h) h is represented
The electric energy output power of moment fuel cell, phFCRepresent the thermal energy output power of h moment fuel cells, ηFC(h) the h moment is represented
The efficiency of fuel cell, γFC(h) h moment fuel cell power energy and thermal energy ratio are represented.
The optimized mathematical model of the present invention is a kind of mixed integer nonlinear programming model, optimization object function be with
One day i.e. 24 hours are a dispatching cycle.
The basic electric appliance constraint is as follows：
A, the electric energy equilibrium of supply and demand is constrained to：
pgrid(h)=peFC(h)+pms(h)+ptm(h)+pdefe(h)+pbatt(h)-ppv(h)
In formula, pgrid(h) the interaction electricity between h moment and major network, p are representedeFC(h) electricity of h moment fuel cells is represented
Energy output power, pms(h) the basic electrical energy demands amount in h moment household energy management systems is represented, i.e., cannot dispatch electric appliance
The electricity consumption of equipment, ptm(h) electricity consumption of h moment air-conditioning equipments, p are representeddefe(h) h moment switching control devices are represented
Electricity consumption, pbatt(h) discharge and recharge of h moment accumulators, p are representedpv(h) generated energy of h moment photovoltaics is represented,The minimum amount of power interacted with power grid and maximum electricity are represented respectively；
B, the thermal energy equilibrium of supply and demand is constrained to：
pgas(h)+phFC(h)=ph(h)
In formula, pgas(h) the direct thermal energy supply of the natural gas at h moment, p are representedhFC(h) h moment fuel cells are represented
Thermal energy supply, ph(h) the thermal demand amount of h moment household energy management systems is represented；
The direct thermal energy supply p of above-mentioned natural gasgas(h) also meet following natural gas straight to be constrained to for thermal energy：
In formula,Represent the maximum direct supply of natural gas；
C, the fuel cell is constrained to：
peFC(h)-peFC(h-1)<peFC,U
peFC(h-1)-peFC(h)<peFC,D
In formula, peFC(h) and peFC(h-1) the electric energy supply of h and h-1 moment fuel cells, p are represented respectivelyeFC,UIt represents
Fuel cell power energy upper limit value, peFC,DRepresent fuel cell power energy lower limiting value,WithRepresent fuel cell most respectively
Small power supply electric energy and maximum power supply electric energy；
D, the accumulator is constrained to：
SOCmin≤SOC(h)≤SOCmax
In formula：The charge volume of h moment accumulators is represented,The discharge capacity of h moment accumulators is represented,The maximum pd quantity of accumulator is represented,Represent the maximum charge amount of accumulator, ηchThe efficiency of accumulator charging is represented,
ηdchRepresent the efficiency of battery discharging, pbatt(h) capacity of battery is represented, E (h) represents accumulator total capacity, and SOC (h) is represented
The state of charge of h moment accumulators, SOCminRepresent the minimum electricity ratio of battery, SOCmaxRepresent the maximum electricity ratio of battery
Example；
E, the air-conditioning equipment is constrained to：
In formula, Tin(h+1),Tin(h) temperature (DEG C) of h, h+1 moment room air is represented respectively, and Δ was represented between the time
Every the time interval Δ of specific implementation is one hour, and RC represents thermal resistance parameters, RC=9.45；ptm(h) heat at h moment is represented
Energy power, Tout(h) outdoor temperature is represented,Indoor minimum and maximum temperature is represented,Represent maximum thermal energy work(
Rate；
F, the switching control device constraint is as follows：
In formula：δa,hRepresent switching control device a in the working condition at h moment, δa,τRepresent work of the load a at the τ moment
State, αa,βaRepresent the working region of switching control device a,Represent switching control device a in current h0The work shape at moment
State, HaRepresent the active length that switching control device a needs are completed,Represent the working condition before the h moment；h0Represent current
Moment, h represent h0At the time of before, τ represents h0At the time of later.
In the optimization object function, the efficiency eta of h moment fuel cellsFC(h) and h moment fuel cell power energy and thermal energy
Compare γFC(h) the following formula calculating is respectively adopted：
In formula, the power load rate of PLR (h) expression h moment fuel cells, i.e. expression electromotive power output and maximum power
Than.
The step 2) is specially：
2.1) N is randomly generatedpopA particle, particle include：
Randomly generate the interaction electricity p between major networkgrid, fuel cell electric energy output power peFC, accumulator fills
Discharge capacity pbatt, air-conditioning equipment electricity consumption ptmWith the direct thermal energy supply p of natural gasgasWith a hour in 24 hours
For the value that unit is changed, it is expressed as xgrid1...xgrid24,xeFC1...xeFC24,xbatt1...xbatt24,xtm1...xtm24,
xgas1...xgas24, it is successive value, xgrid1...xgrid24It is expressed as in 24 hours one day and the interaction electricity of major network,
xeFC1...xeFC24It is expressed as the electric energy supply of fuel cell in 24 hours one day, xbatt1...xbatt24It is expressed as one day 24 small
When in accumulator discharge and recharge, xtm1...xtm24The electrical demand of air-conditioning equipment in 24 hours one day is expressed as,
xgas1...xgas24It is expressed as deliverability of gas in 24 hours one day...；
The on off state that three switching control devices were changed in 24 hours as unit of a hour is randomly generated,
It is expressed asRepresent switching control device 24 hours 1 one days
In on off state,Represent on off state of the switching control device in 24 hours 2 one days,It represents
On off state of the switching control device in 24 hours 3 one days, due to being [0,1] binary condition so being centrifugal pump；
The dimension of each particle is as follows：
2.2) desired value and binding occurrence are calculated：Bringing all particles into optimized mathematical model, to calculate each particle corresponding
Desired value and promise breaking value calculate as follows：
Desired value is calculated as：
Promise breaking value is represented with vF, is calculated as being unsatisfactory for the value of following constraints：
Wherein, j represents the ordinal number of promise breaking value promise breaking possibility, and m represents the sum of promise breaking value promise breaking possibility, m=6, wj
Represent the weight of j-th of promise breaking value, FjRepresent j-th of promise breaking value；
Wherein, Fj, j ∈ 1 ... 6 } be respectively：
F1=| Pgrid(h)-pbatt(h)+ppv(h)-peFC(h)-pms(h)-ptm(h)-pdefe(h)|
F2=| pgas(h)+phFC(h)-ph(h)|
F3=| peFC(h)-peFC(h-1)-peFC,U|
F4=| peFC(h-1)-peFC(h)-peFC,D|
Wherein, F1、F2、F3、F4、F5And F6Foot with thumb down respectivelypgas(h)+phFC(h)
=ph(h)、peFC(h)-peFC(h-1)<peFC,U、peFC(h-1)-peFC(h)<peFC,D、SOCmin≤SOC(h)≤SOCmaxWithPromise breaking value during constraint, referred to as first, second, third, fourth, the five, the 6th
Promise breaking value；
2.3) judge whether vF is zero, if 2.4) zero enters step, it is laggard otherwise to carry out promise breaking value reparation flow
Enter step 2.4)；
2.4) N is builtcA hybrid particle swarm number is gone forward side by side travelingization：
2.4.1 the fitness value as particle) is added with promise breaking value using the desired value of each particle, with fitness value from small
To being ranked up to all particles greatly；
2.4.2 it) carries out processing successively to each particle according to sequence and obtains hybrid particle swarm, specifically find and current grain
Other two nearest particle of sub- Euclidean distance forms a hybrid particle swarm, then by three in the hybrid particle swarm newly formed
A particle is rejected from sequence, and hybrid particle swarm is obtained followed by processing is carried out to next particle in sequence；
2.4.3 diversified processing) is carried out to hybrid particle swarm：
For each hybrid particle swarm, the various angle value D in inside of hybrid particle swarm is calculated using the following formulai,
AD (i) represents the average distance of particle in mixing group i
If internal various angle value DiMeet It represents to preset various threshold value, then using the following formula to stuff and other stuff
Group carries out diverging processing：
Wherein,Represent various degree pre-set value,WithFitness value maximum in i-th of hybrid particle swarm is represented respectively
Two particles, xi_bestRepresent that particle of fitness value minimum in i-th of hybrid particle swarm, φ is a random number；
To all hybrid particle swarms after diverging is handled, then carry out mirror image processing：
Wherein,Represent the particle after mirror image processing in i-th of hybrid particle swarm,It represents in i-th of hybrid particle swarm
Random value between fitness value two particles of minimum, xi_worstRepresent maximum that of fitness value in i-th of hybrid particle swarm
A particle, RstepRepresent mirror image step；
By three particles in each new particle fitness value in hybrid particle swarm after mirror image processing and former hybrid particle swarm
The maximum value of fitness value be compared judgement, meet the following formula, then replaced with the new particle after mirror image processing former mixed
Close the maximum corresponding particle of fitness value in population：
Wherein,Represent the fitness value of particle after i-th of hybrid particle swarm mirror image processing, f (xi_wors t) represent i-th
The fitness value of the particle of fitness value maximum in a hybrid particle swarm；
2.4.4) to step 2.4.3) obtain hybrid particle swarm and independent particle be updated；
First for the optimal particle x in independent particle and hybrid particle swarmpioneerIt is updated, independent particle refers to own
After particle ownership hybrid particle swarm, the remaining particle not belonged to any hybrid particle swarm that gets off, optimal particle xpioneer
Refer to the particle of fitness value minimum in each hybrid particle swarm；
xi(h+1)=xi(h)+vi(h+1)
Wherein, vi(h+1)、vi(h) chalk represents particle in the speed at h+1 and h moment, xi(h+1)、xi(h) it represents respectively
Value of the particle at h+1 the and h moment, ω are inertial factor, c1And c2For first, second weighted value,Represent particle i itself most
Excellent particle represents the optimal particle that particle i occurred in calculating process, pgbestRepresent the optimal particle in all particles,
Rand expressions randomly generate the number between [0,1]；
Then optimal particle x in hybrid particle swarm is calculatedpioneerChanging value, updated in the hybrid particle swarm with changing value
Other two particles；
2.5) repeat the above steps 2.2-2.4), p is obtained in real timebestAnd pgbestAnd it updates, pbestIt evolves for current particle
Minimum fitness value in the process, pgbestFor the minimum fitness value during all particle evolutions；
If minimum fitness value pbestAnd pgbestContinuous 10 sub-value change be less than 0.005, then stop repeat step, with
The solution of fitness value minimum finally obtained is optimal variable parameter.
Promise breaking value in the step 2.3) repairs flow：
2.3.1) structure promise breaking expression formula vCj,j∈{1,...6}；
vC1=| Pgrid(h)-pbatt(h)+ppv(h)-peFC(h)-pms(h)-ptm(h)-pdefe(h)|
vC2=| pgas(h)+phFC(h)-ph(h)|
vC3=| peFC(h)-peFC(h-1)-peFC,U|
vC4=| peFC(h-1)-peFC(h)-peFC,D|
Wherein, vC1、vC2、vC3、vC4、vC5And vC6Foot row expression formula with thumb down respectively
pgas(h)+phFC(h)=ph(h)、peFC(h)-peFC(h-1)<peFC,U、peFC(h-1)-peFC(h)<peFC,D、SOCmin≤SOC(h)
≤SOCmaxWithPromise breaking expression formula during constraint；
2.3.2 it) calculates and repairs adjusted value Δ x, expression formula is as follows：
Wherein,It represents to the expression formula vC that breaks a contractjDerivation；
2.3.3 x values) are repaired：The value of x is updated by the following formula；
xnew=x+ Δs x
Wherein, solutions of the x for primary particle, xnewSolution for particle after reparation.
Household electrical appliance are divided into two classes by the present invention, and one kind is controllable burden, and one kind is uncontrollable load.For uncontrollable negative
The household electrical appliance of lotus, load cannot adjust.And in the present invention, the equipment for participating in optimization does not include uncontrollable load,
Such as corresponding basic household electrical appliance.And switch Control Cooling is regarded as general equipment, i.e., it can only control the equipment
Push And Release.
The accumulator of the present invention can realize charge and discharge in different situations in family is energy-optimised, such as can be in height
Battery discharge when peak electricity consumption, and when low ebb, it charges the battery, so as to avoid peak of power consumption, final realize is reduced
Financial expenditure purpose.
The air-conditioning equipment control main purpose of the present invention is control indoor temperature, is made in the comfortable temperature of user.
The fuel cell of the present invention is the equipment of a thermoelectricity mixing, can provide thermal energy and electricity simultaneously by the equipment
Energy.
The major network of the present invention refers to the supply network of electric power supply plant, can by power grid come buy electricity or according to power grid not
Electricity price of the same period avoids peak of power consumption so as to fulfill cuing down expenses.
Compared with prior art, the present invention it has the following advantages：
Difference is to be based on Mixed integer linear programming with the mathematical model established in the past, and this method is excellent home energy
Change problem changes into more complicated, a more accurate mixed integer nonlinear programming problem, while is adopted on method for solving
With evolutionary computation method is improved, flow is repaired to improve convergence speed of the algorithm using promise breaking, is carried using hybrid particle swarm group
The diversity of high algorithm, in the hope of more excellent solution.
Inventive algorithm does not need to specific computing environment simultaneously, is more suitable for running on electronic product.
Description of the drawings
Fig. 1 is the structure chart of household energy management system.
Specific embodiment
The present invention will be further described below in conjunction with the accompanying drawings.The present embodiment carries out reality based on the technical solution of the present invention
It applies, gives detailed embodiment and specific operating process, but protection scope of the present invention is not limited to following embodiments.
It is as follows according to the embodiment and its implementation process of the content of present invention：
1) optimized mathematical model for the Energy Management System that founds a family, optimized mathematical model is with entire home energy management system
The minimum target as an optimization of one day total electricity bill spending and optimization object function is established, while establish the pact of optimization object function in system
Beam condition, constraints are constrained including basic electric appliance, and basic electric appliance constraint includes the constraint of the electric energy equilibrium of supply and demand, the thermal energy equilibrium of supply and demand
Constraint, fuel cell constraint, accumulator constraint, air-conditioning equipment constraint and switching control device constraint.
The household energy management system includes fuel cell, accumulator, air-conditioning equipment, switching control device, air-conditioning
Equipment, switching control device are all connected to the power grid being mainly made of fuel cell, accumulator, major network and photovoltaic
In, fuel cell is also connected to thermal hardware, and fuel cell provides thermal energy for thermal hardware, and natural gas connects fuel cell respectively
And thermal hardware, natural gas provide natural gas energy resource for fuel cell so that natural gas energy resource is converted into heat in fuel cell
Energy and electric energy, natural gas provide natural gas energy resource so that natural gas energy resource is converted into itself needs by thermal hardware for thermal hardware
Thermal energy.
In household energy management system, basic electric energy device, fuel cell, accumulator, major network and solar energy have been further included
Photovoltaic is powered jointly for basic electric energy device, air-conditioning equipment and switching control device, and power supply volume insufficient section is mended by major network
Fill power supply.
2) input optimized mathematical model need parameter, using improve improve evolution algorithm to the optimized mathematical model into
Row solve, obtain optimal variable parameter, according to optimal variable parameter in household energy management system fuel cell, electric power storage
Pond, air-conditioning equipment, switching control device carry out optimum control, and obtain minimum electricity charge spending.
2.1) first initiation parameter：N is setpopA population initializes pgrid(h) working region where
Value, initialize ppv(h) value, the relevant parameter SOC of initialization accumulatormin,SOCmax,Ebatt,ηch,ηdch,
Value, initialization fuel cell relevant parameter peFC,U, peFC,D,Initialize air-conditioning equipment relevant parameterTout(h) and maximum powerInitialize the quantity N of controllable deviceaAnd relevant parameter αa,βa, HaAnd work
Power Pa, initialization basic electricity pms(h) value.
The present embodiment parameter is set as：Npop=200,ppv(h) the at will prediction of certain day
Value, for this example is chosen between [0,2KW], SOCmin=0.3, SOCmax=0.9, Ebatt=6.86KW, ηch=0.9, ηdch=
0.9,peFC,U=1.5, peFC,D=1.5, Tout
(h) data of certain day are randomly selected,Na=3, αa,βaValue at random generate [1,24] in randomly generate, Pa
Size randomly generates between [1KW, 3KW], pms(h) value is simulated peak of power consumption between [0,2] and is randomly generated, ph(h) with
Machine generates the number between [0,2], pgas(h)=2.
Then N is randomly generatedpopA particle；
2.2) desired value and binding occurrence are calculated：Bringing all particles into optimized mathematical model, to calculate each particle corresponding
Desired value and promise breaking value；
2.3) judge whether vF is zero, if 2.4) zero enters step, otherwise enter step and 2.6) carry out promise breaking value
It is entered step 2.4) after repairing flow；
2.4) N is builtcA hybrid particle swarm number is gone forward side by side travelingization：
2.4.1 the fitness value as particle) is added with promise breaking value using the desired value of each particle, with fitness value from small
To being ranked up to all particles greatly,
2.4.2 it) carries out processing successively to each particle according to sequence and obtains hybrid particle swarm, specifically find and current grain
Other two nearest particle of sub- Euclidean distance forms a hybrid particle swarm, then by three in the hybrid particle swarm newly formed
A particle is rejected from sequence, and hybrid particle swarm is obtained followed by processing is carried out to next particle in sequence；
2.4.3 diversified processing) is carried out to hybrid particle swarm：
In arranging in fact,The value of 0.05, φ is taken to be generated between [2.0,5.0], RstepTake 6.0；,
2.4.4) to step 2.4.3) obtain hybrid particle swarm and independent particle be updated；
First for the optimal particle x in independent particle and hybrid particle swarmpioneerIt is updated, independent particle refers to own
After particle ownership hybrid particle swarm, the remaining particle not belonged to any hybrid particle swarm that gets off, optimal particle xpioneer
Refer to the particle of fitness value minimum in each hybrid particle swarm；
Calculating process：Each hybrid particle swarm quantity is 3, and total population is 200, so Nc=(200-1)/3=
66, and remaining independent particle number=200-3*66=2.
Parameter is set as during being updated：ω=0.73, c1=2, c2=2.
Then optimal particle x in hybrid particle swarm is calculatedpioneerChanging value, updated in the hybrid particle swarm with changing value
Other two particles；
2.5) repeat the above steps 2.2-2.4), p is obtained in real timebestAnd pgbestAnd it updates, pbestIt evolves for current particle
Minimum fitness value in the process, pgbestFor the minimum fitness value during all particle evolutions；
If minimum fitness value pbestAnd pgbestContinuous 10 sub-value change be less than 0.005, then stop repeat step, with
The solution of fitness value minimum finally obtained is optimal variable parameter；
2.6) promise breaking value repairs flow：
2.6.1) calculate promise breaking value vF and promise breaking expression formula vCj,j∈{1,...6}；
vC1=| Pgrid(h)-pbatt(h)+ppv(h)-peFC(h)-pms(h)-ptm(h)-pdefe(h)|
vC2=| pgas(h)+phFC(h)-ph(h)|
vC3=| peFC(h)-peFC(h-1)-peFC,U|
vC4=| peFC(h-1)-peFC(h)-peFC,D|
Wherein vC1、vC2、vC3、vC4、vC5And vC6Foot row expression formula with thumb downpgas
(h)+phFC(h)=ph(h)、peFC(h)-peFC(h-1)<peFC,U、peFC(h-1)-peFC(h)<peFC,D、SOCmin≤SOC(h)≤
SOCmaxWithWhen promise breaking expression formula；
2.6.2 the value of Δ x) is calculated；Δ x represents reparation adjusted value, its expression formula is as follows：
WhereinIt represents to the expression vC that breaks a contractj, the derivation of j ∈ { 1 ... 6 }；
2.6.3 x values) are repaired：The value of x is updated by the following formula；
xnew=x+ Δs x
Wherein, solutions of the x for primary particle, xnewSolution for particle after reparation.
The result situation of the present embodiment is：
The performance of this algorithm is compared with business optimization software tool knitro, and knitro is run on AMPL platforms,
By the operation of 10 times, as a result such as following table：
| Optimization software | Operation result |
| knitro | 44.89RMB |
| This algorithm | 45.86RMB |
Finally, the result of this algorithm and business optimization software knitro are close, can substitute business optimization software
To solve the complex mathematical model of foundation.
Claims (6)
- It is 1. a kind of based on the home energy data optimization methods for improving evolution algorithm, which is characterized in that including：1) optimized mathematical model for the Energy Management System that founds a family, optimized mathematical model is in entire household energy management system The minimum target as an optimization of total electricity bill spending in one day simultaneously establishes optimization object function, while establish the constraint item of optimization object function Part, constraints are constrained including basic electric appliance, and basic electric appliance constraint includes the constraint of the electric energy equilibrium of supply and demand, the thermal energy equilibrium of supply and demand about Beam, fuel cell constraint, accumulator constraint, air-conditioning equipment constraint and switching control device constraint；2) parameter that input optimized mathematical model needs seeks the optimized mathematical model using evolution algorithm is improved Solution, obtains optimal variable parameter, according to optimal variable parameter to fuel cell, accumulator, the sky in household energy management system Equipment, switching control device is adjusted to carry out optimum control, and obtains minimum electricity charge spending.
- 2. according to claim 1 a kind of based on the home energy data optimization methods for improving evolution algorithm, feature exists In：The household energy management system include fuel cell, accumulator, air-conditioning equipment, switching control device, air-conditioning equipment, Switching control device is all connected in the power grid being mainly made of fuel cell, accumulator, major network and photovoltaic, fuel Battery is also connected to thermal hardware, and natural gas connects fuel cell and thermal hardware respectively.
- 3. according to claim 1 a kind of based on the home energy data optimization methods for improving evolution algorithm, feature exists In：The expression formula of the optimization object function is：In formula, C represents the electricity charge spending of one day；Tgrid(h) electricity price at h moment, p are representedgrid(h) it represents between h moment and major network Exchange of electric power amount；Tgas(h) Gas Prices at h moment, p are representedgas(h) represent that the direct thermal energy of the natural gas at h moment supplies Ying Liang；PeFC(h) the electric energy output power of h moment fuel cells, p are representedhFCRepresent the thermal energy output work of h moment fuel cells Rate, ηFC(h) efficiency of h moment fuel cells, γ are representedFC(h) h moment fuel cell power energy and thermal energy ratio are represented.
- 4. according to claim 1 a kind of based on the home energy data optimization methods for improving evolution algorithm, feature exists In：The basic electric appliance constraint is as follows：A, the electric energy equilibrium of supply and demand is constrained to：pgrid(h)=peFC(h)+pms(h)+ptm(h)+pdefe(h)+pbatt(h)-ppv(h)In formula, pgrid(h) the interaction electricity between h moment and major network, p are representedeFC(h) electric energy for representing h moment fuel cells is defeated Go out power, pms(h) the basic electrical energy demands amount in h moment household energy management systems, p are representedtm(h) h moment sky is represented Adjust the electricity consumption of equipment, pdefe(h) electricity consumption of h moment switching control devices, p are representedbatt(h) h moment accumulators are represented Discharge and recharge, ppv(h) generated energy of h moment photovoltaics is represented,The minimum electricity interacted with power grid is represented respectively Amount and maximum electricity；B, the thermal energy equilibrium of supply and demand is constrained to：pgas(h)+phFC(h)=ph(h)In formula, pgas(h) the direct thermal energy supply of the natural gas at h moment, p are representedhFC(h) heat of h moment fuel cells is represented Energy supply, ph(h) the thermal demand amount of h moment household energy management systems is represented；The direct thermal energy supply p of above-mentioned natural gasgas(h) also meet following natural gas straight to be constrained to for thermal energy：In formula,Represent the maximum direct supply of natural gas；C, the fuel cell is constrained to：peFC(h)-peFC(h-1)<peFC,UpeFC(h-1)-peFC(h)<peFC,DIn formula, peFC(h) and peFC(h-1) the electric energy supply of h and h-1 moment fuel cells, p are represented respectivelyeFC,URepresent fuel Battery power upper limit value, peFC,DRepresent fuel cell power energy lower limiting value,WithRepresent that the minimum of fuel cell supplies respectively Electric energy and maximum power supply electric energy；D, the accumulator is constrained to：SOCmin≤SOC(h)≤SOCmaxIn formula：The charge volume of h moment accumulators is represented,The discharge capacity of h moment accumulators is represented, The maximum pd quantity of accumulator is represented,Represent the maximum charge amount of accumulator, ηchRepresent the efficiency of accumulator charging, ηdch Represent the efficiency of battery discharging, pbatt(h) capacity of battery is represented, E (h) represents accumulator total capacity, when SOC (h) represents h Carve the state of charge of accumulator, SOCminRepresent the minimum electricity ratio of battery, SOCmaxRepresent the maximum electricity ratio of battery；E, the air-conditioning equipment is constrained to：Tin min≤Tin(h)≤Tin maxIn formula, Tin(h+1),Tin(h) temperature (DEG C) of h, h+1 moment room air is represented respectively, and Δ represents time interval, RC tables Show thermal resistance parameters, RC=9.45；ptm(h) the thermal energy power at h moment, T are representedout(h) outdoor temperature is represented,Generation Table is indoor minimum and maximum temperature,Represent maximum thermal energy power；F, the switching control device constraint is as follows：In formula：δa,hRepresent switching control device a in the working condition at h moment, δa,τRepresent working conditions of the load a at the τ moment, αa,βaRepresent the working region of switching control device a, δa,h0Represent switching control device a in current h0The working condition at moment, Ha Represent the active length that switching control device a needs are completed,Represent the working condition before the h moment；h0When representing current It carves, h represents h0At the time of before, τ represents h0At the time of later.
- 5. according to claim 1 a kind of based on the home energy data optimization methods for improving evolution algorithm, feature exists In：In the optimization object function, the efficiency eta of h moment fuel cellsFC(h) compare γ with h moment fuel cell power energy and thermal energyFC (h) the following formula calculating is respectively adopted：In formula, PLR (h) represents the power load rate of h moment fuel cells, that is, represents electromotive power output and the ratio of maximum power.
- 6. according to claim 1 a kind of based on the home energy data optimization methods for improving evolution algorithm, feature exists In：The step 2) is specially：2.1) N is randomly generatedpopA particle, particle include：Randomly generate the interaction electricity p between major networkgrid, fuel cell electric energy output power peFC, accumulator charge and discharge Measure pbatt, air-conditioning equipment electricity consumption ptmWith the direct thermal energy supply p of natural gasgasUsing a hour as list in 24 hours The value that position is changed, is expressed as xgrid1...xgrid24,xeFC1...xeFC24,xbatt1...xbatt24,xtm1...xtm24, xgas1...xgas24, xgrid1...xgrid24It is expressed as in 24 hours one day and the interaction electricity of major network, xeFC1...xeFC24It is expressed as The electric energy supply of fuel cell, x in 24 hours one daybatt1...xbatt24It is expressed as the charge and discharge of accumulator in 24 hours one day Amount, xtm1...xtm24It is expressed as the electrical demand of air-conditioning equipment in 24 hours one day, xgas1...xgas24It is expressed as 24 hours one day In deliverability of gas...；The on off state that three switching control devices were changed in 24 hours as unit of a hour is randomly generated, is represented ForRepresent switching control device in 24 hours 1 one days On off state,Represent on off state of the switching control device in 24 hours 2 one days,Represent switch On off state of the control device in 24 hours 3 one days；2.2) desired value and binding occurrence are calculated：It brings all particles into optimized mathematical model and calculates the corresponding target of each particle Value and promise breaking value calculate as follows：Desired value is calculated as：Promise breaking value is represented with vF, is calculated as being unsatisfactory for the value of following constraints：Wherein, j represents the ordinal number of promise breaking value promise breaking possibility, and m represents the sum of promise breaking value promise breaking possibility, m=6, wjRepresent the The weight of j promise breaking value, FjRepresent j-th of promise breaking value；Wherein, Fj, j ∈ 1 ... 6 } be respectively：F1=| Pgrid(h)-pbatt(h)+ppv(h)-peFC(h)-pms(h)-ptm(h)-pdefe(h)|F2=| pgas(h)+phFC(h)-ph(h)|F3=| peFC(h)-peFC(h-1)-peFC,U|F4=| peFC(h-1)-peFC(h)-peFC,D|Wherein, F1、F2、F3、F4、F5And F6Foot with thumb down respectivelypgas(h)+phFC(h)=ph (h)、peFC(h)-peFC(h-1)<peFC,U、peFC(h-1)-peFC(h)<peFC,D、SOCmin≤SOC(h)≤SOCmaxWithPromise breaking value during constraint, referred to as first, second, third, fourth, the five, the 6th Promise breaking value；2.3) judge whether vF is zero, if 2.4) zero enters step, otherwise carry out entering step after promise breaking value repairs flow It is rapid 2.4)；2.4) N is builtcA hybrid particle swarm number is gone forward side by side travelingization：2.4.1 the fitness value as particle) is added with promise breaking value using the desired value of each particle, with fitness value from small to large All particles are ranked up；2.4.2 it) carries out processing successively to each particle according to sequence and obtains hybrid particle swarm, specifically find and current particle Europe Other two closest particle of formula forms a hybrid particle swarm, then by three grains in the hybrid particle swarm newly formed Son is rejected from sequence, and hybrid particle swarm is obtained followed by processing is carried out to next particle in sequence；2.4.3 diversified processing) is carried out to hybrid particle swarm：For each hybrid particle swarm, the various angle value D in inside of hybrid particle swarm is calculated using the following formulai,AD (i) represents the average distance of particle in mixing group iIf internal various angle value DiMeet Represent preset various threshold value, then using the following formula to hybrid particle swarm into Row diverging is handled：Wherein,Represent various degree pre-set value,WithFitness value is maximum in i-th of hybrid particle swarm two are represented respectively Particle, xi_bestRepresent that particle of fitness value minimum in i-th of hybrid particle swarm, φ is a random number；To all hybrid particle swarms after diverging is handled, then carry out mirror image processing：Wherein,Represent the particle after mirror image processing in i-th of hybrid particle swarm,It represents to adapt in i-th of hybrid particle swarm Random value between angle value two particles of minimum, xi_worstRepresent that grain of fitness value maximum in i-th of hybrid particle swarm Son, RstepRepresent mirror image step；By in each new particle fitness value in hybrid particle swarm after mirror image processing and former hybrid particle swarm three particles it is suitable The maximum value of angle value is answered to be compared judgement, meets the following formula, then replaces former mangcorn with the new particle after mirror image processing The maximum corresponding particle of fitness value in subgroup：Wherein,Represent the fitness value of particle after i-th of hybrid particle swarm mirror image processing, f (xi_worst) represent to mix for i-th Close the fitness value of the particle of fitness value maximum in population；2.4.4) to step 2.4.3) obtain hybrid particle swarm and independent particle be updated；First for the optimal particle x in independent particle and hybrid particle swarmpioneerIt is updated, independent particle refers to all particles After ownership hybrid particle swarm, the remaining particle not belonged to any hybrid particle swarm that gets off, optimal particle xpioneerRefer to The particle of fitness value minimum in each hybrid particle swarm；Then optimal particle x in hybrid particle swarm is calculatedpioneerChanging value, its in the hybrid particle swarm is updated with changing value His two particles；2.5) repeat the above steps 2.2-2.4), p is obtained in real timebestAnd pgbestAnd it updates, pbestFor in current particle evolutionary process Minimum fitness value, pgbestFor the minimum fitness value during all particle evolutions；If minimum fitness value pbestAnd pgbestContinuous 10 sub-value change be less than 0.005, then stop repeat step, with final The solution of the fitness value minimum of acquisition is optimal variable parameter.
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