CN110165665A - A kind of source-lotus-storage dispatching method based on improvement multi-objective particle swarm algorithm - Google Patents
A kind of source-lotus-storage dispatching method based on improvement multi-objective particle swarm algorithm Download PDFInfo
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Abstract
The present invention discloses a kind of based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, step includes: that thermal power plant and clean energy resource power plant, customer charge, energy storage device polymerization are become source-lotus-storage and dispatch system by S1., determines the source-lotus-storage scheduling system optimizing scheduling objective function and constraint condition with system operation cost minimum and clean energy resource consumption amount maximum；S2. the particle variable bound section using the optimizing scheduling objective function as the fitness function for improving multi-objective particle swarm algorithm, using the constraint condition as algorithm；S3. according to the fitness function and particle variable bound section, using improvement multi-objective particle swarm algorithm, the particle variable for obtaining the minimum value of the fitness function and being minimized the fitness function.The present invention has many advantages, such as that implementation method is simple, flexible in application, can effectively improve the solution efficiency of power scheduling scheme, can obtain more reasonable, the better power scheduling scheme of economic benefit.
Description
Technical field
The present invention relates to technical field of electric power system control more particularly to a kind of based on improving multi-objective particle swarm algorithm
Source-lotus-storage dispatching method.
Background technique
The northern area of China photovoltaic power generation and wind-power electricity generation are quickly grown in recent years, a large amount of clean energy resourcies it is grid-connected certain
Energy crisis is alleviated in degree.However heating period in winter, to meet thermal load demands, a large amount of fired power generating units work " with
Under the mode of the fixed electricity of heat ", the electricity power output of unit depends on heating demand, and the electricity power output of heating period fired power generating unit in winter is caused to be adjusted
Energy saving power is very limited.This coupled thermomechanics relationship makes electric system peak modulation capacity insufficient, and system usually requires to give up
Part clean energy resource or cut-out load ensure stable operation, thereby resulted in mass energy waste and reduce power supply
Reliability.
Schedulable resource in source-lotus-storage scheduling system includes clean energy resource power plant power output, fired power generating unit, electric boiler, use
Family electric load, user's thermic load, electric storage device, heat-storing device.Electric system needs to meet power-balance, when electric system can not
When dissolving the clean energy resource of access, system needs discard portion clean energy resource.Information of the grid dispatching center as entire power grid
Maincenter is responsible for predicting time daily load and clean energy resource power output situation according to weather and account of the history, be supplied in guarantee electricity, thermic load
In the case where answering, and take into account the operating cost of electric system, formulate corresponding scheduling scheme, by power-supply device in system,
The schedulable resource such as customer charge, energy storage device is adjusted, and improves clean energy resource consumption amount, at the same reduce the operation of system at
This.
In order to obtain optimal scheduling scheme, in recent years, multi-objective particle swarm algorithm is due to its solution efficiency high feature
It is widely applied in the multi-objective optimization question research of electric system, but that there is also optimizing late convergences simultaneously is slow, holds
The problem of easily falling into local optimum, solution efficiency and solving precision can no longer meet the demand of modern power systems scheduling.
Therefore, it is necessary to further further study the method for solving of the optimization problem of electric system, improve its solution efficiency and ask
Precision is solved, to obtain more reasonable, the better power scheduling scheme of economic benefit.
Summary of the invention
The technical problem to be solved in the present invention is that, for technical problem of the existing technology, the present invention provides one
Kind can effectively improve the solution efficiency of power scheduling scheme, help to obtain more reasonable, the better power scheduling of economic benefit
Scheme, and implementation method is simple, flexible in application based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm.
In order to solve the above technical problems, technical solution proposed by the present invention are as follows:
A kind of source-lotus-storage dispatching method based on improvement multi-objective particle swarm algorithm, step include:
S1. thermal power plant and clean energy resource power plant, customer charge, energy storage device polymerization are become source-lotus-storage and dispatch system,
Source-the lotus-storage scheduling system optimizing scheduling target is determined with system operation cost minimum and clean energy resource consumption amount maximum
Function and constraint condition；
S2. using the optimizing scheduling objective function as the fitness function for improving multi-objective particle swarm algorithm, with described
Particle variable bound section of the constraint condition as algorithm；
S3. according to the fitness function and particle variable bound section, using improving multi-objective particle swarm algorithm,
The particle variable for obtaining the minimum value of the fitness function and being minimized the fitness function.
As a further improvement of the present invention, by thermal power plant and clean energy resource power plant, customer charge, storage in the step S1
Energy device aggregation, which becomes source-lotus-storage, dispatches system, operating cost, the operating cost of electric boiler, energy storage with specific reference to thermal power plant
The factors such as the operating cost of device, the scheduling cost of customer charge and clean energy resource consumption amount construct source-lotus-storage system optimization and adjust
Model is spent, and the source-lotus-storage system Optimized Operation mould is determined with system operation cost minimum and clean energy resource consumption amount maximum
The objective function and constraint condition of type.
As a further improvement of the present invention, the objective function of the source-lotus-storage system Optimal Operation Model is specifically pressed
Formula is calculated:
Wherein, f1The operating cost of system, f are dispatched for source-lotus-storage2For clean energy resource consumption amount, fgen、fD、fS、fDRPoint
Not Wei thermal power plant operating cost, the operating cost of electric boiler, the operating cost of energy storage device, the scheduling cost of customer charge,
Pw,tFor the output power of clean energy resource power plant, and meet
Wherein, PGi,t、HGi,tThe electricity power output and heat power output of fired power generating unit i, P respectively in thermal power plantD,tFor the fortune of electric boiler
Row power, Psto,t、Prel,tThe respectively charge power and discharge power of electric storage device, Hsto,t、Hrel,tRespectively heat-storing device
Endothermic power and heat release power, Δ PDR,tFor the user's electric load variable quantity for participating in scheduling, Tt inFor the indoor tune of user's thermic load
Save temperature.
As a further improvement of the present invention, the constraint condition of the source-lotus-storage system Optimal Operation Model includes power
Constraints of Equilibrium, the constraint of thermal power unit operation characteristic, electric boiler operation constraint, energy storage device operation constraint, customer charge response are about
Beam, for characterizing the demand status of fired power generating unit, electric boiler, the operating status of energy storage device and customer charge.
As a further improvement of the present invention, with the mesh of the source-lotus-storage system Optimal Operation Model in the step S2
Scalar functions improve multi-objective particle swarm algorithm model as the fitness function for improving multi-objective particle swarm algorithm, building；With institute
Source-lotus-storage system Optimal Operation Model constraint condition is stated as particle variable bound section, according to the particle variable bound
Section generates N group source-lotus-storage system scheduling scheme at random and becomes as the primary particle for improving multi-objective particle swarm algorithm model
Amount.
As a further improvement of the present invention, it is calculated in the step S3 using multi-objective particle swarm algorithm is improved
The minimum value of the fitness function and the particle variable for being minimized the fitness function, specifically calculate step are as follows:
S31. setting improves the calculating parameter of multi-objective particle swarm algorithm, is given birth at random according to particle variable bound section
At N number of primary particle variable, constituent particle population；
S32. each particle in particle populations is calculated using the fitness function for improving multi-objective particle swarm algorithm to become
The fitness value of amount filters out minimum fitness value f in particle populationslowAnd the corresponding particle variable of minimum fitness value
xlow, as particle populations global optimum fitness and global optimum's particle variable；
S33. inertia weight is obtained according to correction formula, correction formula may be expressed as:
Wherein,Respectively i-th of particle is in the kth time revised inertia weight of optimizing and needs increased inertia
Weight disturbance quantity；wmax、wminFor maximum, minimum inertia weight；kmaxFor maximum modified number, and meet
Wherein,For inertia weight disturbance quantity, fi kIt is i-th of particle in the modified fitness of kth time；It is whole
A particle populations average fitness and global optimum fitness corresponding in kth time amendment.
S34. particle variable is modified more according to certain rule using inertia weight and particle populations more new formula
Newly, it is continuously available new particle variable, particle populations more new formula may be expressed as:
Wherein, i=1,2 ..., m (m is particle populations scale)；J=1,2 ..., n (number that n is variable)；Point
Not Wei corresponding j-th of the variable of particle i in kth time optimizing correct corresponding position and speed；Respectively j-th of variable
Corresponding personal best particle and global optimum position after kth time optimizing amendment；c1、c2For Studying factors；r1、r2To obey 0
~1 equally distributed independent random number.
S35. newly generated particle variable is calculated using the fitness function for improving multi-objective particle swarm algorithm's
Fitness value filters out minimum fitness value f ' in new generation particle populationslowAnd the corresponding particle variable of minimum fitness value
x′low；Judge its fitness value f 'lowThe minimum fitness value f whether being less than in original particle populationslow；If satisfied, then updating
Particle populations global optimum fitness and global optimum's particle variable, i.e., by newly generated particle variable x 'lowAs global optimum
Particle variable is new to generate minimum fitness value f ' in particle populationslowAs particle populations global optimum fitness；If not satisfied,
Then follow the steps S36；
S36. judge whether times of revision k reaches maximum modified number kmax, if not satisfied, then return step S33；If full
Foot, thens follow the steps S37.
S37. particle populations global optimum fitness and global optimum's particle variable are exported.
As a further improvement of the present invention, it is calculated in the step S3 using improvement multi-objective particle swarm algorithm
Particle populations global optimum particle variable is exactly source-lotus-storage scheduling system optimal electrical power scheduling scheme；What is be calculated is described
Particle populations global optimum fitness is exactly source-lotus-storage system Optimal Operation Model objective function minimum value, i.e. source-lotus-storage
Minimum operating cost of the scheduling system under the optimal electrical power scheduling scheme and maximum clean energy resource consumption amount.
As a further improvement of the present invention, the source-lotus-storage scheduling system optimal electrical power scheduling scheme refers to
The power output situation of each unit in source-lotus-storage scheduling system, i.e. the electricity power output situation of fired power generating unit and heat power output situation, grill pan
The dispatch situation of the operating condition of furnace, the consumption amount of clean energy resource, the operating condition of energy storage device, customer charge.
As a further improvement of the present invention, the clean energy resource power plant refers to that wind power plant, photovoltaic plant etc. are novel clear
One or more of clean energy power plant.
Compared with the prior art, the advantages of the present invention are as follows:
1) improvement multi-objective particle swarm algorithm provided by the invention uses and changes by the inertia weight of times of revision exponential damping
Become strategy, overcomes the deficiency of linear attenuation weight, can be improved calculating speed；Inertia weight disturbance term is introduced simultaneously, is passed through
More adjacent modified twice particle populations corresponding fitness adjusts inertia weight, and being effectively improved algorithm, to solve the later period easy
In the defect for falling into local optimum, compared to traditional solution method, be conducive to the solution efficiency for reinforcing algorithm, so that solving electricity
When Force system Optimal Scheduling, more reasonable, the better power scheduling scheme of economic benefit can be quickly obtained.
2) present invention dispatches system to thermal motor by building source-lotus-storage for the electric system of access clean energy resource
Group, electric boiler, energy storage device, customer charge carry out coordinated scheduling, in the case where guaranteeing user's heat supply, electricity consumption satisfaction, into
One step expands clean energy resource and dissolves space, increases clean energy resource consumption amount, improves efficiency of energy utilization.
3) thermal power plant and clean energy resource power plant, customer charge, energy storage device polymerization are become source-lotus-storage and dispatched by the present invention
Source-lotus-storage scheduling system Optimized Operation scheme is calculated using multi-objective particle swarm algorithm is improved, according to optimization in system
The operation conditions of each equipment, can enhance source-lotus-storage system tune in the-lotus-storage scheduling system of scheduling scheme coordinated control source
Flexibility is spent, energy utilization rate is can effectively improve, reduces carbon emission and coal consumption, reduce the operating cost of electric system.
4) source-lotus-storage scheduling system can be quickly calculated by improving multi-objective particle swarm algorithm in the present invention
Optimized Operation scheme, solution effect is good, speed is fast, is not only able to the controlling party of each equipment in the-lotus-storage scheduling system of offer source
Case, additionally it is possible to source-lotus under the scheduling scheme-storage scheduling system operating cost is provided, system can be dispatched for source-lotus-storage
Operation provides good decision support.
Detailed description of the invention
Fig. 1 is the embodiment of the present invention based on the source-lotus-storage dispatching method implementation process for improving multi-objective particle swarm algorithm
Schematic diagram.
Fig. 2 is the Optimized Operation schematic illustration of source of the embodiment of the present invention-lotus-storage scheduling system.
Fig. 3 is the embodiment of the present invention based on the model solution process schematic for improving multi-objective particle swarm algorithm.
Fig. 4 is clean energy resource power curve and user's electricity, thermic load curve synoptic diagram in the specific embodiment of the invention.
Fig. 5 is the operating cost and the signal of clean energy resource consumption rate convergence curve of two kinds of algorithms in the specific embodiment of the invention
Figure.
Specific embodiment
Below in conjunction with Figure of description and specific preferred embodiment, the invention will be further described, but not therefore and
It limits the scope of the invention.
As shown in Figure 1, the embodiment of the present invention is based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, step
Include:
S1. thermal power plant and clean energy resource power plant, customer charge, energy storage device polymerization are become source-lotus-storage and dispatch system,
Source-the lotus-storage scheduling system optimizing scheduling target is determined with system operation cost minimum and clean energy resource consumption amount maximum
Function and constraint condition；
S2. using the optimizing scheduling objective function as the fitness function for improving multi-objective particle swarm algorithm, with described
Particle variable bound section of the constraint condition as algorithm；
S3. according to the fitness function and particle variable bound section, using improving multi-objective particle swarm algorithm,
The particle variable for obtaining the minimum value of the fitness function and being minimized the fitness function.
As shown in Fig. 2, " source side " refers to the tradition electricity such as the clean energy resourcies such as wind-powered electricity generation, photovoltaic and fired power generating unit, electric boiler
Source device；" load side " refers to thering is user's electricity of control characteristic, thermic load；" energy storage side " refers to storage and heat accumulation dress
It sets.Electric boiler is added in source side to consume electric energy production thermal energy, can reduce thermic load peak value that fired power generating unit undertakes
Gentle electric load curve valley simultaneously；Storage and heat-storing device are added in energy storage side, is translated in time using its working characteristics
Electricity, thermic load achieve the purpose that thermoelectricity decouples by source side and the coordinated operation of energy storage side.Economic incentives hand is utilized in load side
Section is regulated and controled to carry out gentle workload demand curve to customer charge, can guarantee user by source-lotus-storage coordination optimization scheduling
In the case where heat supply, electricity consumption satisfaction, bigger space is provided for consumption clean energy resource.Source-the lotus of the present embodiment-storage scheduling system
The Optimized Operation basic principle of system is as shown in Figure 2.
In the present embodiment, thermal power plant and clean energy resource power plant, customer charge, energy storage device polymerization are become source-by step S1
System is dispatched in lotus-storage, with specific reference to the operating cost of thermal power plant, the operating cost of electric boiler, the operating cost of energy storage device, is used
The factors such as the scheduling cost and clean energy resource consumption amount of family load construct source-lotus-storage system Optimal Operation Model, and are transported with system
Row cost minimization and clean energy resource consumption amount maximum determine the objective function peace treaty of the source-lotus-storage system Optimal Operation Model
Beam condition.
In the present embodiment, source-lotus-storage system Optimal Operation Model of foundation include fired power generating unit model, electric boiler model,
Energy-storage system model, customer charge model, clean energy resource dissolve model.Fired power generating unit model is used to characterize the operation of fired power generating unit
Cost；Electric boiler model is used to characterize the operating cost of electric boiler；Energy-storage system model be used to characterize the operation of energy storage device at
This；Customer charge model is used to characterize the scheduling cost of customer charge；Clean energy resource consumption model is for characterizing in electric system
The consumption amount of the clean energy resourcies such as wind-powered electricity generation, photovoltaic.
In this specific embodiment, the generating set of fired power generating unit model is cogeneration of heat and power type unit, considers fired power generating unit
Fuel cost, start-up and shut-down costs and operation expense, the operating cost of fired power generating unit are expressed as shown in formula (1):
In formula (1), fgenFor the operating cost of fired power generating unit, T is the scheduling slot number of whole cycle, NgFor thermoelectricity
Unit number of units；PGi,t、HGi,tThe electricity power output and heat power output of respectively fired power generating unit i；a1,iTo a6,iFor the energy consumption letter of fired power generating unit i
Number is fitted to the unit energy consumption coefficient after quadratic function；For the start-up and shut-down costs coefficient of fired power generating unit i, UGi,tAnd UGi,t-1Respectively
Indicate fired power generating unit i in the start and stop state of t and t-1 period；Indicate the unit power operation expense system of fired power generating unit i
Number.
In this specific embodiment, thermal energy is converted electrical energy into using electric boiler to meet thermal load demands, by opening at any time
Stop and adjust heats power to adapt to thermal load demands variation, advantageously reduces the thermic load that fired power generating unit undertakes, enhancing system
The flexibility of system operation；Increase the electric load of low-valley interval simultaneously, the power generation regulating power of further expansion fired power generating unit can
Play the effect of dual peak regulation.The operation expense and equipment investment depreciable cost of electric boiler are considered in the present embodiment, then
The operating cost of electric boiler is expressed as shown in formula (2) to formula (3):
In formula (2) and formula (3), fDFor the operating cost of electric boiler, HD,t、PD,tRespectively the heats power of electric boiler and
Electric power；For the electric conversion efficiency of electric boiler；Indicate the unit power operation expense coefficient of electric boiler；PDN、nd, r indicate electric boiler unit capacity cost, installed capacity, depreciable life, basic discount rate.
In this specific embodiment, the customer charge in electric system includes traditional load and flexible load, wherein conventional negative
Charge values refer to that not interruptable rigid load, flexible load refer to that the response that can regulate and control by economic incentives means is negative
Lotus.The controllability of load side spatially is realized with flexible thermic load by adjusting flexible electric load, to reach load side
The effect of peak regulation.According to the characteristic of flexible electric load, the scheduling cost of flexible electric load is indicated as shown in formula (4):
In formula (4), fEDRFor the scheduling cost of flexible electric load；ΔPDR,tThe flexible electric load of scheduling is participated in for the t period
Variable quantity, value are timing, represent electric load increase, when value is negative, represent electric load reduction；CEDRTo participate in the soft of scheduling
Property electric load unit compensation cost coefficient.
In this specific embodiment, the comfortable ambiguity of temperature is increased using the time-delay characteristics and heat user of heat supply network heat supply
The soft readjustment ability of heating load.It influences user temperature comfort level to need to compensate to user, the scheduling of flexible thermic load
Cost is indicated as shown in formula (5) to formula (8):
|Tt in-T0|≤σ (6)
fDR=fEDR+fHDR (8)
It is neutralized in formula (8) in formula (5), fDRFor the total activation cost of flexible load；fEDR、fHDRRespectively flexible electric load and
The scheduling cost of flexible thermic load；HL,tFor the thermal load demands amount in system；S is area of heat-supply service, m2；C is unit area of heat-supply service
Under thermal capacitance, preferred value 1.63 × 105J/m2℃；ω be building internal-external temperature difference coefficient of heat transfer, preferably value 1.037 ×
105J/m2℃；Tt in、Tt outThe respectively period indoor and outdoor t temperature is converted into user's thermic load using formula (8) and temperature data
Demand；T0For user's initial set temperature, preferably 20 DEG C of value；σ is temperature controlled variable；γ is to adjust in per area per room
The subsidy cost coefficient of temperature.
In this specific embodiment, cooperated using storage, heat-storing device and fired power generating unit and participate in peak-load regulating, can achieve and turn
User's electricity of peak period, the purpose of thermic load are moved, the regulating power of fired power generating unit can be effectively improved.Consider storage, heat accumulation
The operation expense and equipment depreciation cost of device, then the operating cost of energy storage device is expressed as such as formula (9) to formula (11) institute
Show:
fS=fESS+fTES (9)
In formula (9) and formula (11), fSFor the operating cost of energy storage device；fESS、fTESRespectively storage, heat-storing device
Operating cost；Psto,t、Prel,tThe respectively charge power and discharge power of electric storage device；Hsto,t、Hrel,tRespectively heat-storing device
Endothermic power and heat release power；CESS、ρESS、HESS、LEAnd CTES、ρTES、HTES、LHRespectively indicate electric storage device and heat-storing device
Unit power operation expense coefficient, unit capacity cost of investment, nominal configuration capacity, service life.
In this specific embodiment, constructed source-lotus-storage system Optimal Operation Model is with system operation cost minimum and clearly
Clean energy consumption amount is up to target, can indicate are as follows:
In formula (12), f1The operating cost of system, f are dispatched for source-lotus-storage2For clean energy resource consumption amount, fgen、fD、
fS、fDRThe respectively tune of the operating cost of thermal power plant, the operating cost of electric boiler, the operating cost of energy storage device, customer charge
Spend cost, Pw,tFor the output power of clean energy resource power plant.
In the present embodiment, source-lotus-storage system Optimal Operation Model constraint condition includes power-balance constraint, thermal motor
The constraint of group operation characteristic, electric boiler operation constraint, energy storage device operation constraint, customer charge response constraint, for characterizing thermoelectricity
Unit, electric boiler, the operating status of energy storage device and customer charge demand status.
In this specific embodiment, electrical power Constraints of Equilibrium and heating power balance constraint be may be expressed as:
In formula (13), PL,t、HL,tElectricity, thermal load demands when respectively the t period is not carried out economic incentives means, remaining
Parameter is defined as above.
In this specific embodiment, customer charge response constraint be may be expressed as:
In formula (14), KPFor single period customer charge response threshold；SPmax、SPminAnd SHmax、SHminIt respectively indicates entire
User's electricity of dispatching cycle, thermic load respond total capacity bound, remaining each parameter is defined as above.
In this specific embodiment, using the objective function of the source-lotus-storage system Optimal Operation Model as changing in step S2
Into the fitness function of multi-objective particle swarm algorithm, building improves multi-objective particle swarm algorithm model；With the source-lotus-storage system
The constraint condition of system Optimal Operation Model is generated as particle variable bound section according to particle variable bound section at random
100 groups of source-lotus-storage system scheduling schemes are as the primary particle variable for improving multi-objective particle swarm algorithm model.
In the present embodiment, using multi-objective particle swarm algorithm is improved in step S3, the fitness function is calculated
Minimum value and the particle variable for being minimized the fitness function are asked based on the model for improving multi-objective particle swarm algorithm
Solution preocess is as shown in figure 3, specifically calculate step are as follows:
S31. setting improves the calculating parameter of multi-objective particle swarm algorithm, is given birth at random according to particle variable bound section
At 100 primary particle variables, constituent particle population；
S32. each particle in particle populations is calculated using the fitness function for improving multi-objective particle swarm algorithm to become
The fitness value of amount filters out minimum fitness value f in particle populationslowAnd the corresponding particle variable of minimum fitness value
xlow, as particle populations global optimum fitness and global optimum's particle variable；
S33. inertia weight is obtained according to correction formula, correction formula may be expressed as:
In formula (15),Respectively i-th of particle is in the kth time revised inertia weight of optimizing and needs to increase
Inertia weight disturbance quantity；wmax、wminFor maximum, minimum inertia weight；kmaxFor maximum modified number, preferably value is 200,
And meet
In formula (16),For inertia weight disturbance quantity, fi kIt is i-th of particle in the modified fitness of kth time；For entire the particle populations average fitness and global optimum fitness corresponding in kth time amendment.
S34. particle variable is modified more according to certain rule using inertia weight and particle populations more new formula
Newly, it is continuously available new particle variable, particle populations more new formula may be expressed as:
In formula (17), i=1,2 ..., m (m is particle populations scale)；J=1,2 ..., n (number that n is variable)； Corresponding j-th of the variable of respectively particle i corrects corresponding position and speed in kth time optimizing；Respectively
J-th of variable corresponding personal best particle and global optimum position after kth time optimizing amendment；c1、c2For Studying factors；r1、
r2To obey 0~1 equally distributed independent random number.
S35. newly generated particle variable is calculated using the fitness function for improving multi-objective particle swarm algorithm's
Fitness value filters out minimum fitness value f ' in new generation particle populationslowAnd the corresponding particle variable of minimum fitness value
x′low；Judge its fitness value f 'lowThe minimum fitness value f whether being less than in original particle populationslow；If satisfied, then updating
Particle populations global optimum fitness and global optimum's particle variable, i.e., by newly generated particle variable x 'lowAs global optimum
Particle variable is new to generate minimum fitness value f ' in particle populationslowAs particle populations global optimum fitness；If not satisfied,
Then follow the steps S36；
S36. judge whether times of revision k reaches maximum modified number kmax, if not satisfied, then return step S33；If full
Foot, thens follow the steps S37.
S37. particle populations global optimum fitness and global optimum's particle variable are exported.
In the present embodiment, using improving the particle populations global optimum particle variable that is calculated of multi-objective particle swarm algorithm
It is exactly source-lotus-storage scheduling system optimal electrical power scheduling scheme；The particle populations global optimum fitness being calculated is just
It is source-lotus-storage system Optimal Operation Model objective function minimum value, i.e., source-lotus-storage scheduling system is in the optimal electrical power tune
Minimum operating cost and maximum clean energy resource consumption amount under degree scheme.
In the present embodiment, the source-lotus-storage scheduling system optimal electrical power scheduling scheme refers to source-lotus-storage scheduling system
It is the power output situation of each unit in system, i.e. the electricity power output situation of fired power generating unit and heat power output situation, the operating condition of electric boiler, clear
The dispatch situation of the consumption amount of the clean energy, the operating condition of energy storage device, customer charge.
In this specific embodiment, this Optimization Scheduling is verified with specific experiment, chooses certain scene fire storage
Combined generating system is as research object, wherein including 3 fired power generating units, total installation of generating capacity 1200MW；Clean energy resource power plant packet
Wind power plant and photovoltaic plant are included, wherein installed capacity of wind-driven power 250MW, photovoltaic installed capacity 50MW, clean energy resource prediction power output song
Line and user's electricity, heat load prediction curve are as shown in Figure 4；The rated capacity of electric boiler is 80MW；Storage, heat-storing device it is specified
Capacity is 80MW；User's electric load unit dispatches 180 yuan/MWh of cost value.Preferable temperature controlled variable σ is 2 DEG C, is preferably used
Family load responding threshold kPIt is 0.2.The major parameter of improved multi-objective particle swarm algorithm is configured that maximum modified number kmax
It is 200, particle number N is 100, maximum inertia weight wmaxIt is 0.9, minimum inertia weight wminIt is 0.4, Studying factors c1、c2?
Take 1.5.
In order to illustrate source-lotus-storage dispatching method advantage based on improvement multi-objective particle swarm algorithm in the present embodiment, if
It has set both of which to compare and analyze, be respectively as follows:
1) traditional mode: source-lotus-storage dispatching method based on traditional multi-objective particle swarm algorithm；
2) improved mode: based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm.
In order to illustrate improved multi-objective particle swarm algorithm processing the present embodiment institute climbing form type validity, and it is traditional
The convergence curve comparing result of multi-objective particle swarm algorithm is as shown in Figure 5.Convergence curve can be seen that 2 kinds of algorithms and exist from figure
Model can be effectively solved after 200 amendments；Traditional multi-objective particle swarm algorithm is about in more than 40 amendments
After start to restrain, although and improved multi-objective particle swarm algorithm starts to restrain after about 70 amendments, its receive
It holds back curve and interim steady rear the phenomenon that declining again occurs, show constantly to seek obtaining more excellent solution with modified progress, embody
Innovatory algorithm improves the ability for jumping out locally optimal solution, and acquires result better than traditional multi-objective particle swarm algorithm, thus
Demonstrate the validity of innovatory algorithm
By above-mentioned experiment it was determined that the present embodiment can be solved effectively using improved multi-objective particle swarm algorithm
The Optimized Operation scheme in source-lotus-storage system Optimal Operation Model, gained source-lotus-storage scheduling system can comprehensively utilize cleaning energy
Schedulable resource in source power plant, electric boiler, thermal power plant, energy-storage system and customer charge is guaranteeing that user's heat supply, electricity consumption are full
In the case where meaning degree, further expansion clean energy resource dissolves space, improves clean energy resource consumption rate, effectively reduces system operation
Cost.
Above-mentioned only presently preferred embodiments of the present invention, is not intended to limit the present invention in any form.Although of the invention
It has been disclosed in a preferred embodiment above, however, it is not intended to limit the invention.Therefore, all without departing from technical solution of the present invention
Content, technical spirit any simple modifications, equivalents, and modifications made to the above embodiment, should all fall according to the present invention
In the range of technical solution of the present invention protection.
Claims (8)
1. a kind of based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, which is characterized in that step includes:
S1. thermal power plant and clean energy resource power plant, customer charge, energy storage device polymerization are become source-lotus-storage and dispatches system, to be
System operating cost minimum and clean energy resource consumption amount maximum determine the source-lotus-storage scheduling system optimizing scheduling objective function
And constraint condition；
S2. using the optimizing scheduling objective function as the fitness function for improving multi-objective particle swarm algorithm, with the constraint
Particle variable bound section of the condition as algorithm；
S3. it is obtained according to the fitness function and particle variable bound section using multi-objective particle swarm algorithm is improved
The minimum value of the fitness function and the particle variable for being minimized the fitness function.
2. according to claim 1 based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, feature exists
In thermal power plant and clean energy resource power plant, customer charge, energy storage device polymerization, which are become source-lotus-storage scheduling, in the step S1 is
System, with specific reference to the tune of the operating cost of thermal power plant, the operating cost of electric boiler, the operating cost of energy storage device, customer charge
The factors such as cost and clean energy resource consumption amount of spending construct source-lotus-storage system Optimal Operation Model, and with system operation cost minimum
The objective function and constraint condition of the source-lotus-storage system Optimal Operation Model are determined with clean energy resource consumption amount maximum.
3. according to claim 2 based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, feature
It is, the objective function of the source-lotus-storage system Optimal Operation Model is specifically calculated as follows to obtain:
Wherein, f1The operating cost of system, f are dispatched for source-lotus-storage2For clean energy resource consumption amount, fgen、fD、fS、fDRRespectively
Operating cost, the operating cost of electric boiler, the operating cost of energy storage device, the scheduling cost of customer charge of thermal power plant, Pw,tFor
The output power of clean energy resource power plant, and meet
Wherein, PGi,t、HGi,tThe electricity power output and heat power output of fired power generating unit i, P respectively in thermal power plantD,tFor the operation function of electric boiler
Rate, Psto,t、Prel,tThe respectively charge power and discharge power of electric storage device, Hsto,t、Hrel,tThe respectively heat absorption of heat-storing device
Power and heat release power, Δ PDR,tFor the user's electric load variable quantity for participating in scheduling, Tt inFor the indoor adjusting temperature of user's thermic load
Degree.
4. according to any one of claims 1 to 3 based on the source-lotus-storage scheduling for improving multi-objective particle swarm algorithm
Method, it is characterised in that: the constraint condition of the source-lotus-storage system Optimal Operation Model includes power-balance constraint, thermal motor
The constraint of group operation characteristic, electric boiler operation constraint, energy storage device operation constraint, customer charge response constraint, for characterizing thermoelectricity
Unit, electric boiler, the operating status of energy storage device and customer charge demand status.
5. according to claim 4 based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, feature
It is, using the objective function of the source-lotus-storage system Optimal Operation Model as improvement multi-objective particle swarm in the step S2
The fitness function of algorithm, building improve multi-objective particle swarm algorithm model；With the source-lotus-storage system Optimal Operation Model
Constraint condition as particle variable bound section, N group source-lotus-storage system is generated according to particle variable bound section at random
Scheduling scheme unite as the primary particle variable for improving multi-objective particle swarm algorithm model.
6. according to claim 5 based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, feature
Be, in the step S3 using improve multi-objective particle swarm algorithm, be calculated the fitness function minimum value and
The particle variable for being minimized the fitness function, specifically calculates step are as follows:
S31. setting improves the calculating parameter of multi-objective particle swarm algorithm, generates N at random according to particle variable bound section
A primary particle variable, constituent particle population；
S32. each particle variable in particle populations is calculated using the fitness function for improving multi-objective particle swarm algorithm
Fitness value filters out minimum fitness value f in particle populationslowAnd the corresponding particle variable x of minimum fitness valuelow, make
For particle populations global optimum fitness and global optimum's particle variable；
S33. inertia weight is obtained according to correction formula, correction formula may be expressed as:
Wherein,Respectively i-th of particle is in the kth time revised inertia weight of optimizing and increased inertia weight is needed to disturb
Momentum；wmax、wminFor maximum, minimum inertia weight；kmaxFor maximum modified number, and meet
Wherein,For inertia weight disturbance quantity, fi kIt is i-th of particle in the modified fitness of kth time；For entire grain
Sub- the population average fitness and global optimum fitness corresponding in kth time amendment.
S34. particle variable is modified update according to certain rule using inertia weight and particle populations more new formula, no
Disconnected to obtain new particle variable, particle populations more new formula may be expressed as:
Wherein, i=1,2 ..., m (m is particle populations scale)；J=1,2 ..., n (number that n is variable)；Respectively
Corresponding j-th of the variable of particle i corrects corresponding position and speed in kth time optimizing；Respectively j-th of variable is
Corresponding personal best particle and global optimum position after k optimizing amendment；c1、c2For Studying factors；r1、r2To obey 0~1
Equally distributed independent random number.
S35. newly generated particle variable is calculated using the fitness function for improving multi-objective particle swarm algorithmAdaptation
Angle value filters out minimum fitness value f ' in new generation particle populationslowAnd the corresponding particle variable x of minimum fitness value
′low；Judge its fitness value f 'lowThe minimum fitness value f whether being less than in original particle populationslow；If satisfied, then updating grain
Sub- population global optimum fitness and global optimum's particle variable, i.e., by newly generated particle variable x 'lowAs global optimum's grain
Sub- variable is new to generate minimum fitness value f ' in particle populationslowAs particle populations global optimum fitness；If not satisfied, then
Execute step S36；
S36. judge whether times of revision k reaches maximum modified number kmax, if not satisfied, then return step S33；If satisfied, then
Execute step S37.
S37. particle populations global optimum fitness and global optimum's particle variable are exported.
7. according to claim 6 based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, feature
It is, using improving the particle populations global optimum particle variable that is calculated of multi-objective particle swarm algorithm just in the step S3
It is source-lotus-storage scheduling system optimal electrical power scheduling scheme；The particle populations global optimum fitness being calculated is exactly
Source-lotus-storage system Optimal Operation Model objective function minimum value, i.e. source-lotus-storage scheduling system are dispatched in the optimal electrical power
Minimum operating cost and maximum clean energy resource consumption amount under scheme.
8. according to claim 7 based on the source-lotus-storage dispatching method for improving multi-objective particle swarm algorithm, feature
It is, the source-lotus-storage scheduling system optimal electrical power scheduling scheme refers to each unit in source-lotus-storage scheduling system
Power output situation, the i.e. consumption of the electricity power output situation of fired power generating unit and heat power output situation, the operating condition of electric boiler, clean energy resource
The dispatch situation of amount, the operating condition of energy storage device, customer charge.
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