CN107482638A - Supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method - Google Patents
Supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
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- H02J3/383—
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- H02J3/386—
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/242—Home appliances
- Y04S20/244—Home appliances the home appliances being or involving heating ventilating and air conditioning [HVAC] units
Abstract
The invention discloses a kind of supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method；The present invention in optimization process first consider can level move load characteristic, then establish and consider source and energy-storage system schedulability, using the output in three class controllables per the period as optimized variable, so that system operation cost is minimum and pollutant emission control expense is at least for Optimized Operation target, the mathematical modeling of foundation multi-objective optimization scheduling a few days ago；The multi-objective particle instructed using " outstanding particle " is solved to the optimization problem, find two points that system operation cost is minimum and pollutant emission control expense is minimum respectively using single objective genetic algorithm, utilize it as " outstanding particle " and go to guide the search direction of multi-objective particle swarm algorithm；The invention provides a kind of effective multiobjective Dynamic Optimization dispatching method, the comprehensive utilization ratio to improving the multipotency coupled system energy, promotes Renewable Energy Development to have certain meaning.
Description
Technical field
The invention belongs to micro-capacitance sensor technical field, in particular for the micro- electricity of supply of cooling, heating and electrical powers type of large scale business synthesis
A kind of net system, and in particular to the supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization scheduling of coordinated scheduling " source-storage-lotus "
Method.
Background technology
Supply of cooling, heating and electrical powers type micro-capacitance sensor combines the advantages of cold-hot-both electric combined supply system and micro-grid system, can
Waste heat caused by miniature gas turbine generating in micro-capacitance sensor is recycled, and uses Absorption Refrigerator system
It is cold, so as to realize cold, heat and electricity triple supply.Supply of cooling, heating and electrical powers type micro-capacitance sensor can be used as a small-scale supply of cooling, heating and electrical powers low pressure to supply
Electric network, it is that electric power or cold and hot energy are supplied in residential quarter, industrial park or shopping centre etc..Wherein, electric load can be by miniature gas
Turbine and bulk power grid coordinated scheduling provide, and for cold heat load, on the one hand can be by the flue gas discharged after gas turbine power generation
Cold heat amount caused by waste heat driving cold/hot water machine of lithium bromide group ensures, on the other hand can by the refrigeration of air-conditioning,
Heat-production functions meet.For the energy supply of user side, either electric load or cooling and heating load, supply of cooling, heating and electrical powers type
Micro-capacitance sensor can provide double shield.
But because supply of cooling, heating and electrical powers type micro-capacitance sensor has the equilibrium relation between the multiple kinds of energy such as hot and cold, electric, also need to examine
The constraintss such as the workload demand of worry system, interactive power constraint, fuel cost.In addition, as the development of intelligent grid is with building
If there are more and more load types that may participate in system call in load side, as electric automobile, accumulation of heat (cold) air-conditioning system with
And intelligent water heater, intelligent washing machine etc..This type load as newly controllable or be able to can be put down under two-way interaction multiplexe electric technology
Move burdened resource to participate in the scheduling of supply of cooling, heating and electrical powers type micro-capacitance sensor, regulate and control from different time scales for power system
Technical support is provided with operation.Therefore, scheduling supply of cooling, heating and electrical powers type micro-capacitance sensor Generation Side controllable, storage how to be synthesized and coordinated
The schedulable resource of energy system and load side, in the case where meeting that operation constraint constrains with device characteristics, obtain meeting not
With application of the scheduling scheme of target call to popularization supply of cooling, heating and electrical powers type micro-capacitance sensor is dispatched, the utilization rate and drop of the energy are improved
Low environment pollution has very important meaning.It is main for the optimizing research of supply of cooling, heating and electrical powers type micro-capacitance sensor both at home and abroad at present
The scheduling to Generation Side " source " and energy storage (electricity, hot/cold) side controllable is concentrated on, it is inadequate to the controllability Study of load side.
Even if there is a small amount of research work to consider influence of the translatable characteristic of load to scheduling result, but only give each period
The different translatable entry/exit quantity of type load, do not provide the period that load specifically translates entry/exit, so as to limit scheduling result
Realizability.Further, since the operation characteristic of energy storage device, the pact of 0-1 variables and Non-linear coupling is introduced to optimization problem
Beam so that the solution of optimization problem becomes more complicated, find effective optimization method be need to solve at this stage it is another
One problem.
The content of the invention
The present invention be directed to large scale business synthesis supply of cooling, heating and electrical powers micro-grid system, make full use of Generation Side, energy storage and
The controllable of load side, it is excellent so that system operation cost is minimum and pollutant emission control expense is at least for Optimized Operation target
Regenerative resource is first dissolved, by the output in miniature gas turbine, battery, the class controllable of accumulation of heat (cold) groove three per the period
As optimized variable.In order to reduce the complexity of optimization problem, solving speed is improved, using Optimized Operation strategy stage by stage
Solution is optimized to it.I.e. first according to the characteristic and quantity of translatable load, preferentially to dissolve regenerative resource as original
Then, using particle swarm optimization algorithm and " with the amount of deciding through consultation, can determining that translatable electric load can translate with Yu Dingdu " strategy
Quantity and translatable entry/exit period.Then using the electric load curve after improving, system operation and mould are being met
Under conditions of type constraint, the mathematical modeling of Multiobjective Optimal Operation a few days ago is established.It is whole in order to effectively solve the Multivariate Mixed
Number nonlinear optimal problem, it is proposed that the multi-objective particle based on " outstanding particle " guidance is to the Optimized Operation
Model is solved.Specifically implement according to following steps：
Step 1, determine target electric load curve, obtain quantity that translatable electric load can translate and it is translatable enter/
The period gone out.
Different electric loads, which is formulated, for the different operational mode of micro-capacitance sensor or running environment translates target.Work as micro-capacitance sensor
System is under grid-connect mode, according to electricity price data, more electric load is arranged when electricity price is relatively low and is use up when electricity price is higher
Amount reduces electric load amount, i.e. target load and the inversely proportional relation of electricity price.When micro-capacitance sensor is in island operation state, in order to
Reduce and abandon honourable index, according to wind light generation data, less electric load is arranged when wind light generation power is less and in scene
More load, i.e. target electric load and honourable output direct proportionality are arranged when power is more.Translatable load optimal model
Object function be represented by：
In formula, T is dispatching cycle；Pobj,tFor the target load of t periods；Ps,tFor the load of t periods after translation；Pf,tFor t
The original predictive load of period.Load translation should also meet：The species of load is constant before and after translation, it is all in dispatching cycle can
The total amount for translating load is constant.In addition, also need to enter row constraint at the time of allowing to be movable into and out to the translatable load of every class.
According to unit number of the translatable load at each moment, how to determine immigration, amount removed and meet that system pair can
The problem of translation load constraint is the key point for solving the micro-capacitance sensor Optimized Operation that translatable load participates in.The present invention proposes
It is a kind of " with the amount of deciding through consultation, to be used for solving problem above in Yu Dingdu " method, be represented by：
M ÷ N=S......Y (2)
In formula, M is a certain component of optimized variable；N is certain value, and its size can depend on the circumstances；S is the business of equation
Value, which determine the unit number that translatable load removes, i.e., above-mentioned " with the amount of deciding through consultation "；Y is the residual value of equation, which determine
The translation nargin of translatable load, i.e., it is above-mentioned " with Yu Dingdu ".For translational movement and translation nargin, herein only to amount removed
Calculate, its immigration amount can be obtained by corresponding amount removed and corresponding translation nargin.
Solution is optimized to formula (1) and (2) using particle swarm optimization algorithm and obtains what translatable electric load can translate
The period of quantity and translatable entry/exit.
Step 2：Determine the principle of supply of cooling, heating and electrical powers type micro-capacitance sensor Optimal Scheduling.
For grid type supply of cooling, heating and electrical powers type micro-capacitance sensor, wind-force and photovoltaic generating system take maximum tracing mode and excellent
First using its energy that generates electricity.Further, since the introducing of accumulation of heat (cold) groove, co-generation unit no longer need to track heat constantly
The change of (cold) load, the scheduling of system can be participated in as free variable.
Step 3：According to the electric load curve after optimization, with reference to known honourable data and cold heat load, according to scheduling
Principle establishes the mathematical modeling of supply of cooling, heating and electrical powers type micro-capacitance sensor Optimal Scheduling.The optimized variable of the Optimal Scheduling is
The output of battery, accumulation of heat (cold) groove and miniature gas turbine at each moment in 24 hours one day.The micro- electricity of supply of cooling, heating and electrical powers type
Two object functions one of net are the total operating cost of system, and one is systemic contamination thing control emission expense i.e. ring
Border cost.
The constraint of the Optimized Operation is broadly divided into two classes：First, device model constrains, such constraint is it is usually because equipment
Caused by the physics limit of operation, it is desirable to force to meet, otherwise can even whole micro-grid system causes permanently in itself to equipment
Infringement.It is this kind of to constrain the charge-discharge electric power constraint for generally including battery and the state-of-charge constraint of battery, accumulation of heat (cold) groove
The gentle exoergic depth of water storage constraint, the power of gas turbine and the constraint of climbing rate etc..It is another kind of to be constrained to system fortune
The constraint that row constraint, i.e. system should meet in operation, this kind of constraint mainly include each moment power and energy balance about
Initial and end time of the dump energy of beam and energy storage device including battery and accumulation of heat (cold) groove within dispatching cycle should
When being consistent,
For the constraint of above two type, the constraint processing method that the present invention uses is also different.For device model about
Beam, the method handled firmly using constraint, is boundary value by the operation variable pressure assignment for running counter to confinement element.And for system
The constraint of power-balance, typically uses dimension-reduction treatment method in operation constraint, that is, assumes there is N number of variable in equation, choose wherein
N-1 variable is as independent variable.A remaining variable is dependent variable, and its value is total to by the value and constraint equation of other independents variable
With determination.And flexible constraint processing method is used for the constraint of property dispatching cycle, the present invention, will using the method for penalty function
The situation for running counter to above-mentioned constraint adds total operating cost as penalty term, so as to form new object function：
Step 4, using the multi-objective particle instructed based on " outstanding particle " the Optimal Operation Model is entered
Row solves.
First with single objective genetic algorithm to the operating cost and Environmental costs of supply of cooling, heating and electrical powers type micro-grid system most
It is low to optimize scheduling respectively for target, and preserve Optimized Operation result.Secondly, after being initialized to multi-objective particle swarm algorithm,
Two individuals two scheduling results that genetic algorithm preserves being assigned at random in particle cluster algorithm population.Finally, more mesh are utilized
Mark particle swarm optimization algorithm optimizes scheduling to system and calculated.
Step 5, output optimization calculate result, i.e., the Pareto forward positions of the total operating cost of system and Environmental costs with
And battery, heat storage tank and miniature gas turbine are in the output of day part.
The inventive method has the advantage that and beneficial outcomes are：
1st, the problem of supply of cooling, heating and electrical powers type micro-capacitance sensor can solve the problem that a large amount of distributed power source access bulk power grids and produce, together
When due to its intelligence and flexible control feature, solving environmental pollution, energy shortage, improving power supply reliability and energy profit
There is great potential with rate etc..In addition, with the development of intelligent meter and intelligent electric appliance, the electric automobile of user side,
The load such as intelligent water heater and intelligent washing machine can participate in cold under two-way interaction multiplexe electric technology as translatable burdened resource
In the scheduling of cogeneration type micro-capacitance sensor, so as to improve flexibility, reliability and the economy of micro-grid system operation.This hair
It is bright when studying supply of cooling, heating and electrical powers type micro-capacitance sensor Optimal Scheduling, considered source, energy-storage system and load side can
Scheduling property, therefore obtained result more meets the requirement of the power system marketization.In addition, provide more mesh using pareto forward positions
The result of Optimized Operation is marked, for multiple target weighting is processed into the result of single-object problem, can be supplied to
Actual motion personnel more select.
2nd, the present invention can obtain in the case of given optimization aim electric load, using " with the amount of deciding through consultation, with Yu Dingdu "
Method can determine the amount of being movable into and out of all kinds of translatable electric loads of each period and the period of corresponding translation.Namely
Say and not only can know that now section is movable into and out the unit number of load, it can also be seen that the load being moved out of is adjusted to specifically
Moment.This is greatly increased for this kind of central controlled supply of cooling, heating and electrical powers type micro-grid system of large scale business synthesis
The operability of scheduling result.
3rd, due to the Optimal Scheduling of supply of cooling, heating and electrical powers type micro-grid system be directed not only to various energy resources stream conversion and
Coupling, also electric energy and cold heat energy energy-storage system, therefore for mathematical angle, are to belong to Multivariate Mixed integral nonlinear
Planning problem so that the conventional algorithm such as traditional interior point method fails, and calculates the methods of MIXED INTEGER, penalty function and smooth function
Efficiency is not good enough.The multi-objective particle that the present invention is instructed using " outstanding particle " solves to the optimization problem,
The advantages of combining particle swarm optimization algorithm and genetic algorithm, so as to improve the calculating speed of algorithm and ability of searching optimum.
Brief description of the drawings
Fig. 1 is the multi-objective particle flow chart provided by the invention instructed based on " outstanding particle "；
Fig. 2 is supply of cooling, heating and electrical powers type micro-capacitance sensor basic structure in example of the present invention；
Fig. 3 is that typical case's day summer is honourable in example of the present invention, electric heating/cooling load prediction is contributed and reality
When electricity price curve；
Fig. 4 is translatable load disinfection cabinet in example of the present invention, washing machine and water heater need hourly
Seek power curve；
Fig. 5 be in example of the present invention supply of cooling, heating and electrical powers type micro-capacitance sensor target electric load curve and optimization after
Electric load curve；
Fig. 6 is the Pareto forward positions figure that system optimization is dispatched in example of the present invention；
Fig. 7 is the output of electric energy unit when system operation cost is minimum in example of the present invention；
Fig. 8 is the output of cold energy unit when system operation cost is minimum in example of the present invention；
Fig. 9 is the output of electric energy unit when environment treatment cost is minimum in example of the present invention；
Figure 10 is the output of cold energy unit when environment treatment cost is minimum in example of the present invention；
Figure 11 is battery and accumulation of heat (cold) groove SOC scheduling result in example of the present invention.
Embodiment
With reference to embodiment, the present invention will be described in detail.
Supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method proposed by the present invention, it is real according to following steps
Apply.
Step 1, determine target electric load curve, obtain quantity that translatable electric load can translate and it is translatable enter/
The period gone out.Comprising the following steps that for algorithm is realized in electric load translation：
(1) basic data is inputted.
Include among these all kinds of translatable electric loads in the unit number at each moment and its electricity consumption characteristic, scheduling a few days ago it is pre-
The electric load data of survey, wind-power electricity generation and photovoltaic generation prediction data and Spot Price.
(2) target electric load is determined according to corresponding mechanism.
When micro-grid system is under grid-connect mode, according to electricity price data, more electric load is arranged when electricity price is relatively low
And reduction load of being tried one's best when electricity price is higher, i.e. target electric load and the inversely proportional relation of electricity price, as shown in formula (1)：
D in formulatFor the electricity price of t in dispatching cycle, Pobj,tFor the target load of t periods, Pf,tFor the original of t periods
Begin prediction load, and T is dispatching cycle.
When micro-capacitance sensor is in island operation state, honourable index is abandoned in order to reduce, can according to wind light generation data,
Less electric load is arranged when wind light generation power is less and more electric load is arranged when honourable power is more, be i.e. target electricity
Load and honourable output direct proportionality, as shown in formula (2)：
In formula, WPtFor t wind-force in dispatching cycle and the pre- power scale of photovoltaic generation.
(3) translatable load model is solved.
Formula (1) or formula (2) are substituted into formula (3), using particle swarm optimization algorithm, and using formula (4) define " with business
It is quantitative, the unit number of translatable electric load removal and the translation nargin of translatable electric load are obtained in Yu Dingdu " method, from
And obtain the specific moment that this period is movable into and out the unit number of load and the load being moved out of is adjusted to；
Wherein formula (3) is
In formula, T is dispatching cycle；Pobj,tFor the target load of t periods；Ps,tFor the load of t periods after translation； Pf,tFor t
The original predictive load of period；Wherein load translation meets：The species of load is constant before and after translation, it is all in dispatching cycle can
The total amount for translating load is constant；Having per the translatable load of class allows the time margin of translation, can be carried out about by the Y in formula (4)
Beam.
Formula (4) is
M ÷ N=S......Y (4)
In formula, M is a certain component of optimized variable；N is certain value；S is the quotient of equation, and which determine translatable negative
The unit number that lotus removes；Y is the residual value of equation, and which determine the translation nargin of translatable load；For translational movement and translate abundant
Degree, only amount removed is calculated, its immigration amount can be obtained by corresponding amount removed and corresponding translation nargin.
Electric load curve after step 2, the principle according to supply of cooling, heating and electrical powers type micro-capacitance sensor Optimal Scheduling, and optimization,
With reference to known honourable data and cold heat load, supply of cooling, heating and electrical powers type micro-capacitance sensor Optimal Scheduling is established according to dispatching principle
Mathematical modeling.One of object function is the total operating cost of system：
f1(X)=JE(X)+JO(X)+JF(X)+JB(X) (5)
In formula, JE(X) it is the energetic interaction cost of micro-grid system and bulk power grid；JO(X) for equipment operation maintenance into
This；JF(X) it is the fuel cost of gas turbine；JB(X) it is the depreciable cost of battery and accumulation of heat/cold trap.
Another object function is that systemic contamination thing control emission expense is Environmental costs：
In formula, n is the species of pollutant；ViFor the control emission expense of i-th pollutant；Qi(X) it is i-th pollutant
Discharge capacity.
Therefore the object function of supply of cooling, heating and electrical powers type micro-capacitance sensor is represented by：
F (X)=min ([f1(X),f2(X)]T) (7)
The constraint of the Optimized Operation is divided into two classes：First, device model constrains, including：
(1) battery
Battery meets that charge-discharge electric power constrains, i.e.,
-PES_ch_max≤PES≤PES_dis_max(8) in formula, PES_ch_maxAnd PES_dis_maxRespectively maximum allowable fills
Electrical power and discharge power, PESIt is negative for battery power, during charging, for just during electric discharge.
In addition, the power constraint of battery is expressed as the constraint of the SOC changes in amplitude of two adjacent moments i.e.：
SOCi+1-SOCi≤δ (9)
In formula, for δ under different running statuses, i.e. the value of charge or discharge state is different.
In addition, it is also necessary to consider the state-of-charge constraint of battery：
SOCmin≤SOCi≤SOCmax (10)
In formula, SOCmax、SOCminThe bound requirement of storage battery charge state is represented respectively.
(2) accumulation of heat/cold trap
Accumulation of heat/cold trap uses horizontal with battery same procedure, the wherein heat accumulation of accumulation of heat/cold trap in the processing of constraint
The rated capacity of battery is correspond to, exoergic depth correspond to the depth of discharge of battery；
(3) gas turbine
Gas turbine power generation power Pgen(t) bound of power should be met：
In formulaFor the minimum startup power of generator；Maximum power generation.
In addition, gas turbine should also meet that climbing rate constrains：
In formula, Pup、PdownRespectively generator bound climbing rate limit value.
Another kind of to be constrained to system operation constraint, i.e., the constraint that system should meet in operation, this kind of constraint includes：
(1) power and energy balance constraint
System should meet power-balance in operation, therefore, meet in each period：
Pload(t)=Pgen(t)+PES(t)+PPV(t)+PWT(t) (13)
In formula, Pload(t)、PPV(t)、PWT(t) electric load power, photovoltaic and wind-force prediction after respectively optimizing generate electricity
Power；PES(t) it is accumulator cell charging and discharging power, is discharged for timing, to be charged when bearing.
Also need to meet that system cold heat can balance simultaneously：
Qload(t)=Qair(t)+Qgt+Qhs (14)
In formula, Qload(t)、Qair(t) it is respectively hot/cold load, air-conditioning heating/cold, QhsThe heat discharged for heat storage tank/
Cold；Discharge hot/cold amount for timing, for it is negative when store hot/cold amount, QgtThere is provided for miniature gas turbine high-temperature tail gas waste heat
Heating/cold.
(2) energy storage device SOC value initial time is identical with finish time
Because supply of cooling, heating and electrical powers type micro-capacitance sensor Optimized Operation is presented periodically, therefore, include battery for energy storage device
It should be consistent with the initial and end time of the state of accumulation of heat/cold trap within dispatching cycle, i.e.,：
SOCstart_b=SOCend_b (15)
SOCstart_q=SOCend_q (16)
In formula, SOCstart_b、SOCend_b、SOCstart_qAnd SOCend_qBattery initial time respectively in dispatching cycle
With the state-of-charge of end time, and accumulation of heat/cold trap initial time and the heat accumulation level for terminating the time.
Constrain, battery power, the startup power and peak power of gas turbine powered generator, use for device model
The method handled firmly is constrained, the operation variable pressure assignment that will run counter to the element of constraint is boundary value.I.e. such as the storage of formula (9)
Shown in battery constraint：
If | SOCi+1-SOCi| ＞ δ (17)
Then
The constraint of power-balance, typically uses dimension-reduction treatment method in being constrained for system operation, that is, assumes have in equation
N number of variable, wherein N-1 variable is chosen as independent variable.A remaining variable is dependent variable, and its value is by other independents variable
Value and constraint equation determine jointly；By Pgrid(t) it is used as dependent variable.
For the energy-storage units in the system operation cycle, initial and end time SOC need to be consistent this constraint and its
Device model constrains bind lines into the mixed constraints with time coupling；Using flexible constraint processing method, will run counter to
Formula (15) and the constraint of (16) add total operating cost as penalty term, so as to form new object function：
F'(X)=F (X)+β | SOCi+1-SOCi| (19)
In formula, β is the SOC constraint penalty factors.F (X) is multiple objective function, i.e., penalty term is incorporated into each sub-goal
On function.
Step 3, using the multi-objective particle instructed based on " outstanding particle " the Optimal Operation Model is entered
Row solves；Comprise the following steps that：
(1) using single objective genetic algorithm to the operating cost and Environmental costs of supply of cooling, heating and electrical powers type micro-grid system most
It is low to optimize scheduling respectively for target, and preserve Optimized Operation result.
(2) population initializes
Multi-objective particle is initialized, including determines number, total iterations, the inertia power of population
The setting of weight values and Studying factors.And two scheduling results for preserving genetic algorithm in step (1) are assigned to population and calculated at random
Two individuals in method population.
(3) fitness value of each particle is calculated
Fitness function is determined, and calculates fitness value corresponding to each particle.Closed according to the domination of each particle in population
System determines the non-domination solution of population, and non-domination solution is put into external archive collection；
(4) sorted according to crowding distance, and delete the particle beyond scale
If not being any limitation as to the scale in outside archive set, new non-domination solution will constantly enter external archive collection
And cause its internal particle that explosive growth is presented, thus reduce the calculating performance of whole algorithm.In addition, in order to keep
The diversity of whole non-domination solution, crowding distance descending sort need to be carried out to the particle in outside archive set.So both it ensure that
The calculating performance of algorithm, it can also retain the diversity of whole population.
(5) the individual extremely optimal and global optimum position of more new particle
The personal best particle and globally optimal solution of more new particle.Determine the method and single goal particle of globally optimal solution
Group's algorithm has very big difference, when the optimal selection of target is determined by Pareto dominance relations, the storage of external archive collection
The non-domination solution found, but do not have but in whole set " absolute " optimal solution.From by crowding distance sequence
External archive concentrates before selection 10 particle, then randomly selects the overall situation of one of particle as this iteration
Optimal solution.
(6) speed of more new particle and position
Particle swarm optimization algorithm is derived from the research of flock of birds predation, and the bird in population is abstracted into one by the algorithm
Individual particle, by the information sharing between these particles with cooperating, follow the particle individual optimal value searched and grain
The global optimum of subgroup updates the position of each particle, by successive ignition finally determines global optimum.Enter in algorithm
During row, PbestiThe optimal optimal value for referring to a particle and finding so far；GbestRefer to that whole population is found so far
Optimal value.The renewal speed of all particles and location formula are in population：
In formula, k is iterations；Vi kFor particle i flying speed；Vi k+1It is that particle i flies in+1 iteration of kth
Speed；c1、c2For Studying factors, value 2；r1、r2It is the random number between [0,1]；ω is inertia weight coefficient, is used
To weigh the search capability of local optimum and global optimum.ω usually requires dynamically to adjust in algorithm, by the iteration in formula (22)
The function of number linear decrease calculates inertia weight ω：
In formula, ωmaxGeneral value 0.9；ωminFor 0.4；K is current iterations；kmaxFor the greatest iteration of setting
Number.
Speed and the position of whole population particle are updated according to formula (20) and (21).The speed of particle and position after renewal
It is possible to beyond given search space scope, the particle beyond search space is now assigned to boundary value and its speed is anti-
To.In order to prevent particle to be absorbed in local optimum, position disturbance is carried out after being chosen to the particle in population according to certain probability.
The selection of probable value should reduce with the increase of iterations, in order to the Fast Convergent in Evolution of Population later stage.
(7) whether end condition is met
Whether evaluation algorithm meets end condition, and correlated results is exported if meeting, otherwise goes to step (3) continuation
Perform.
Step 4, the result that finally calculates of output, the i.e. total operating cost of system and environmental improvement cost the two targets
Between Pareto forward positions.Miniature gas turbine, battery, accumulation of heat/cold trap, air-conditioning are obtained on this basis in per period
Contribute and interact electrical power with bulk power grid.
Embodiment
The supply of cooling, heating and electrical powers type micro-capacitance sensor shown in Fig. 2 is chosen herein, and the micro-capacitance sensor operates in and net state, dispatching cycle
For one day, unit scheduling time Δ t was one hour.The photovoltaic generation of typical case's day summer, wind-power electricity generation, electric load, heat are (cold) negative
The pre- power scale and Spot Price of lotus are as shown in Figure 3.To ensure the efficient utilization of the energy, the waste heat cigarette of miniature gas turbine
Gas all supplies cold/hot water machine of lithium bromide group.Miniature gas turbine rated output power is 60kW, minimum startup power
For 18kW, generating efficiency 0.3, radiation loss coefficient is 0.16, the use of fuel is natural gas, its calorific value is 9.7kWh/
m3, price is 3.3 yuan/m3；The coefficient of performance of refrigerating of lithium bromide absorption type cooling and heating unit is 1.2, coefficient of performance in heating 0.9；Electricity
The refrigeration and coefficient of performance in heating of air-conditioning are 2.7；The charge and discharge rate of battery and heat storage tank is 0.9,0.05 yuan of depreciable cost/
(kWh), capacity 200AH, two adjacent moment SOC changing values are up to 0.3；The pollutant of micro-grid system and bulk power grid
Emission factor and corresponding cost are as shown in table 1：
The pollutant discharge coefficient of table 1 and processing cost
The translatable load initial distribution of table 2
1st, translatable load mainly includes disinfection cabinet, washing machine and electric heater.Wherein, the running hours of disinfection cabinet
Between be one hour, washing machine stream time be two hours, electric heater stream time is three hours, and can be put down
It is different to move load each hour demand power in stream time section.In total load, the accounting of translatable load
Probably 30% or so, real needs power parameter is as shown in Figure 4 per hour.The translatable load cell of three classes in dispatching cycle
Number initial distribution is as shown in table 2.
Determine target electric load curve using formula (3), select per the translatable load of class each scheduling slot quantity for
Optimized variable, it is 72 to set particle dimension.Obtain what translatable electric load can translate using the particle swarm optimization algorithm of classics
The period of quantity and translatable entry/exit.It is 200 to set particle populations scale, iterations 1000, utilizes single goal grain
Subgroup optimized algorithm solves the optimization problem of the Prescribed Properties of formula (1) and (3) description.Obtain the strategy of optimization translation load
As shown in table 3.
The translatable load shift strategy of table 3
It is illustrated as 68 using the shift strategy of the first moment disinfection cabinet in table 3, is understood according to formula (2), when N takes 7,
68 divided by 7, business 9, remainder 5." with the amount of deciding through consultation "：The quantity of disinfection cabinet now is 5, therefore it is 5*9/ that it, which moves quantity,
10=4.5, according to the principle to round up, now the removal quantity of disinfection cabinet should be 5, i.e., all removes." with Yu Dingdu "：
Remainder be 5, it is necessary to which the translatable load of this period is moved into five hours after, shift strategy solve finish.It follows that
By the value for changing divisor N, you can change the maximum of translation nargin.The distribution of the translatable load cell number of three classes after movement
As shown in table 4：
The translatable load cell number distribution after translating of table 4
Using the prediction load of typical case's day summer, supply of cooling, heating and electrical powers type micro-grid load translation result is as shown in Figure 5.By
Fig. 5 can be seen that, although the load curve after translation with target load curve co-insides, compared to predicting load curve, its
Warp-wise target load direction is close.In terms of translation afterload curve fluctuation fluctuations, also than prediction load curve more
It is smooth.In addition, the peak valley power of original predictive load is respectively 121.4kW and 40.2kW, translation afterload peak valley power point
Not Wei 110.8kW and 68.2kW, peak valley difference value reduce, therefore, serve the effect of peak load shifting.
2nd, according to the electric load curve after specific data and translation, grid-connected cold and hot Electricity Federation is established using formula (5)-(19)
For the mathematical modeling of the multi-objective optimization question of type micro-capacitance sensor.
3rd, the particle populations scale for setting multi-objective particle is 200, iterations 1000, battery
Initial SOC value and accumulation of heat (cold) groove initial time heat accumulation it is horizontal be 0.4, be by what is obtained using single objective genetic algorithm
Total operating cost minimum point and the environmental improvement expense minimum point of uniting as two of multi-objective particle initially
Particle, solution is optimized using multi-objective particle.The step 3 that specific solution procedure is shown in embodiment.
The Optimized Operation result arrived includes electric energy list when the Pareto forward positions of Multiobjective Optimal Operation, target minimum with operating cost
Electric energy unit and heat (cold) energy each equipment of unit when the output of member and each equipment of thermal unit, target minimum with operating cost
Output and energy-storage units SOC each moment within dispatching cycle value, as illustrated in figs. 6-11.
Scheduling result shows that the scope of operating cost is [891.1 yuan 1663.0 yuan], and the scope of Environmental costs is
[297.0 yuan 827.7 yuan].Compared to 1051.1 yuan of minimum operation cost in system call result before load translation, save
60 yuan, it can be seen that panning effect is obvious.It is primarily due to the load that can translate for being in high electricity price being moved to
At low electricity price, the expense that system buys electricity to bulk power grid is thus reduced.Because this target load is according to electricity price
Fluctuate and set, and the economic sexual intercourse of electricity price and system operation is more close, therefore relative to the minimum ring before translation load
300.7 yuan of border cost does not have greatly improved.
Scheduling result when taking system operation cost minimum is analyzed, such as Fig. 7, shown in 8.In Fig. 7 as can be seen that by
This can be seen that battery mainly experienced charge and discharge process twice in whole dispatching cycle.Wherein, 4,5 points of morning the two when
Between section, now bulk power grid electricity price is relatively low, and battery is charged.And 12 noon is arrived to 15 points of this periods of afternoon, electric power storage
Pond is mostly in discharge condition and targetedly selects to be transported with peak power when electricity price is of a relatively high within these periods
OK.As shown in Figure 11, after 15 battery dischargings terminate, its SOC value reaches lower limit, has released whole electricity, has been filled for system
Divide and backspread.Miniature gas turbine starts between 11 points at 16 points in afternoon, and this is primarily due to time period bulk power grid
Caused by electricity price is too high.By the price of natural gas and the generating efficiency of miniature gas turbine, the valency often to generate a kilowatt
Lattice are equivalent to 0.63 yuan, and the electricity price of this period is all higher than 0.63 yuan.At 12,14,15,16 these moment, by miniature combustion
Gas-turbine sends the electricity released with battery and meets outside the needs of electric load that remaining electricity is then sold to bulk power grid jointly.Cause
This is filled with high electricity price releasing by low electricity price and backspreaded in this period, battery, the warp that miniature gas turbine is generated electricity with it
Ji advantage gets profit.18 compare with 19 electricity prices low state when battery charge, changed from battery SOC, this
Period battery operates in electric power storage state with peak power, and energy reserve is done to be discharged during high electricity price at night.This period it is big
Power network will not only provide the electricity needed for electric load, and electric power storage battery storage capacity and electric air-conditioning institute subfam. Spiraeoideae are all undertaken by bulk power grid,
Therefore the power of the two period bulk power grids is higher, has reached more than 200kW.And stored to the high rate period of 21 to 22 points at night
Battery discharge, miniature gas turbine have been turned on again when electricity price is higher than 0.63 yuan.
Electric air-conditioning converts electric energy to cold or heat energy, sent out with accumulation of heat (cold) groove and miniature gas turbine as variable load
The equilibrium of supply and demand of cold heat load in the common regulating system of fume afterheat caused by electricity.(cold) energy of heat in whole dispatching cycle is single
First scheduling result such as Fig. 8.As shown in Figure 8, heat storage tank is in energy storage state at 5,18,19 points, and 12,14,21,22 points in putting
Can state；Fume afterheat driving lithium-bromide absorption-type refrigerating machine working condition caused by miniature gas turbine generating and miniature combustion
The running status of gas-turbine is consistent, i.e., in running order 11,12,13,14,15,16,20,21.The fortune of accumulation of heat (cold) groove
Row state is similar to battery, but total charge and discharge amount is relatively less.On the one hand be accumulation of heat (cold) groove compared with battery, its energy
Amount transfer and storage income are to release the price difference of earning at a low price by high price accumulation of energy without so obvious.For example, the refrigeration of air-conditioning
(heat) coefficient of performance is 2.7, it means that electric air-conditioning consumption 1kW*h electricity " can carry " 2.7kW*h cold (heat) amount.By
This make it that heat storage tank is relatively inexpensive when shifting cold (heat) energy.On the other hand, the depreciable cost of accumulation of heat (cold) groove also causes to this
A part influences, if it is desired to earns profit, the income for releasing energy is greater than the summation of low price energy storage and depreciable cost.Cause
, in the case where price difference itself is relatively small, it is earned the space of profit and also accordingly reduced for this.When electricity price is higher, due to
The startup of miniature gas turbine, refrigeration duty in system is most of to absorb remaining hot-cast socket by lithium-bromide absorption-type refrigerating machine
Cold undertakes, and part vacancy refrigeration duty on this basis is then by electric air-conditioning and accumulation of heat (cold) groove shared；In electricity price phase
To it is relatively low when, refrigeration duty is all met by electric air conditioner refrigerating amount sometimes, such as at 1,2,3,4,6,7,8,9,17,23, is had
When electricity air-conditioning also carry accumulation of heat (cold) groove be filled with refrigeration duty, for example, at 5,18,19.
The scheduling result of system is analyzed when taking Environmental costs minimum.As shown in figs. 9-11.As shown in Figure 11, due to
The environmental advantage that miniature gas turbine generates electricity relative to bulk power grid, within whole dispatching cycle, is in miniature gas turbine formula
Starting state.In 1 point to 13 period, system and bulk power grid to exchange power almost nil.This period, net load are less than micro-
The rated power of type gas turbine, the vacancy power of system can be undertaken completely by the latter.Because refrigeration duty is of a relatively high, micro-
Cold caused by fume afterheat driving lithium bromide refrigerator caused by type gas turbine power generation can not still meet refrigeration duty, thus
The cold that caused remaining vacancy refrigeration duty is provided by electric air-conditioning meets, and the electricity needed for electric air-conditioning then derives from this period
Miniature gas turbine institute generated energy.After 13 points, due to the increase of net load, even if miniature gas turbine is with rated power
Operation can not still meet the power shortage of whole system, now can only provide dump power by bulk power grid.Battery is in net load
Less than miniature gas turbine rated power when, often in charged state, this part is charged the electricity of battery by miniature combustion
Gas-turbine provides, and its main cause is can try one's best reduction system and the Power Exchange of bulk power grid.When net load is more than miniature combustion
During the rated power of gas-turbine, part vacancy power can be provided by battery and avoid being provided by bulk power grid, that is, meet this part
The electricity of vacancy power is sent by miniature gas turbine, and battery simply serves transportation, and miniature gas turbine is sent out
The Environmental costs of electricity are fewer than bulk power grid, thereby reduce the Environmental costs of system.As shown in Figure 10, due to miniature gas turbine
Startup, air-conditioning is in smaller power running status.The effect of accumulation of heat (cold) groove in the entire system, on the one hand can pass through
It is that accumulation of heat (cold) high electricity price is released so as to improve the economy of whole system in low electricity price, on the other hand, to micro-gas-turbine
The electrothermal load that machine is sent is decoupled, and makes electric energy and hot (cold) can be each independent, improves system control flexibility.When being
During system object run minimum with Environmental costs, accumulation of heat (cold) groove mainly plays second.
In addition, as shown in Figure 11, at 1 with 25 points (in order to distinguish the time value started dispatching cycle with the end of) i.e.
1 point during finishing scheduling is identical.Therefore system call result meets energy-storage units SOC value and should initially protected with the end of in scheduling
Hold consistent requirement.
Claims (1)
1. supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method, it is characterised in that：This method specifically includes following
Step：
Step 1, target electric load curve is determined, obtain quantity that translatable electric load can translate and translatable entry/exit
Period；Comprising the following steps that for algorithm is realized in electric load translation：
(1) basic data is inputted；
Include all kinds of translatable electric loads among these in the unit number at each moment and its electricity consumption characteristic, the electricity for dispatching prediction a few days ago
Load data, wind-power electricity generation and photovoltaic generation prediction data and Spot Price；
(2) target electric load is determined according to corresponding mechanism；
When micro-grid system is under grid-connect mode, according to electricity price data, more electric load is arranged when electricity price is relatively low and
Load, i.e. target electric load and electricity price inversely proportional relation are reduced when electricity price is higher as far as possible, as shown in formula (1)：
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Load is surveyed, T is dispatching cycle；
When micro-capacitance sensor is in island operation state, honourable index is abandoned in order to reduce, can be according to wind light generation data, in scene
Less electric load is arranged when generated output is less and more electric load is arranged when honourable power is more, be i.e. target electric load and wind
Light output direct proportionality, as shown in formula (2)：
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In formula, WPtFor t wind-force in dispatching cycle and the pre- power scale of photovoltaic generation；
(3) translatable load model is solved；
Formula (1) or formula (2) are substituted into formula (3), using particle swarm optimization algorithm, and using formula (4) define " with the amount of deciding through consultation,
The unit number of translatable electric load removal and the translation nargin of translatable electric load are obtained in Yu Dingdu " method, so as to obtain
The specific moment that this period is movable into and out the unit number of load and the load being moved out of is adjusted to；
Wherein formula (3) is
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</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
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<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
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</mrow>
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In formula, T is dispatching cycle；Pobj,tFor the target load of t periods；Ps,tFor the load of t periods after translation；Pf,tFor the t periods
Original predictive load；Wherein load translation meets：The species of load is constant before and after translation, all translatable negative in dispatching cycle
The total amount of lotus is constant；Having per the translatable load of class allows the time margin of translation, can enter row constraint by the Y in formula (4)；
Formula (4) is
M ÷ N=S......Y (4)
In formula, M is a certain component of optimized variable；N is certain value；S is the quotient of equation, and which determine the removal of translatable load
Unit number；Y is the residual value of equation, and which determine the translation nargin of translatable load；It is only right for translational movement and translation nargin
Amount removed calculates, and its immigration amount can be obtained by corresponding amount removed and corresponding translation nargin；
Electric load curve after step 2, the principle according to supply of cooling, heating and electrical powers type micro-capacitance sensor Optimal Scheduling, and optimization, with reference to
Known honourable data and cold heat load, the number of supply of cooling, heating and electrical powers type micro-capacitance sensor Optimal Scheduling is established according to dispatching principle
Learn model；One of object function is the total operating cost of system：
f1(X)=JE(X)+JO(X)+JF(X)+JB(X) (5)
In formula, JE(X) it is the energetic interaction cost of micro-grid system and bulk power grid；JO(X) it is the operation expense of equipment；JF
(X) it is the fuel cost of gas turbine；JB(X) it is the depreciable cost of battery and accumulation of heat/cold trap；
Another object function is that systemic contamination thing control emission expense is Environmental costs：
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<mi>f</mi>
<mn>2</mn>
</msub>
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<munderover>
<mo>&Sigma;</mo>
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<mo>=</mo>
<mn>1</mn>
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<mi>V</mi>
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In formula, n is the species of pollutant；ViFor the control emission expense of i-th pollutant；Qi(X) it is the row of i-th pollutant
High-volume；
Therefore the object function of supply of cooling, heating and electrical powers type micro-capacitance sensor is represented by：
F (X)=min ([f1(X),f2(X)]T) (7)
The constraint of the Optimized Operation is divided into two classes：First, device model constrains, including：
(1) battery
Battery meets that charge-discharge electric power constrains, i.e.,
-PES_ch_max≤PES≤PES_dis_max (8)
In formula, PES_ch_maxAnd PES_dis_maxRespectively maximum allowable charge power and discharge power, PESFor battery power,
It is negative during charging, for just during electric discharge；
In addition, the power constraint of battery is expressed as the constraint of the SOC changes in amplitude of two adjacent moments i.e.：
SOCi+1-SOCi≤δ (9)
In formula, for δ under different running statuses, i.e. the value of charge or discharge state is different；
In addition, it is also necessary to consider the state-of-charge constraint of battery：
SOCmin≤SOCi≤SOCmax (10)
In formula, SOCmax、SOCminThe bound requirement of storage battery charge state is represented respectively；
(2) accumulation of heat/cold trap
Accumulation of heat/cold trap is used in the processing of constraint and correspond to battery same procedure, the heat accumulation level of wherein accumulation of heat/cold trap
The rated capacity of battery, exoergic depth correspond to the depth of discharge of battery；
(3) gas turbine
Gas turbine power generation power Pgen(t) bound of power should be met：
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In formulaFor the minimum startup power of generator；Maximum power generation；
In addition, gas turbine should also meet that climbing rate constrains：
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<mo>-</mo>
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<mo>-</mo>
<mrow>
<mo>(</mo>
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In formula, Pup、PdownRespectively generator bound climbing rate limit value；
Another kind of to be constrained to system operation constraint, i.e., the constraint that system should meet in operation, this kind of constraint includes：
(1) power and energy balance constraint
System should meet power-balance in operation, therefore, meet in each period：
Pload(t)=Pgen(t)+PES(t)+PPV(t)+PWT(t) (13)
In formula, Pload(t)、PPV(t)、PWT(t) electric load power, photovoltaic and wind-force prediction generated output after respectively optimizing；
PES(t) it is accumulator cell charging and discharging power, is discharged for timing, to be charged when bearing；
Also need to meet that system cold heat can balance simultaneously：
Qload(t)=Qair(t)+Qgt+Qhs (14)
In formula, Qload(t)、Qair(t) it is respectively hot/cold load, air-conditioning heating/cold, QhsFor the hot/cold amount of heat storage tank release；
Discharge hot/cold amount for timing, for it is negative when store hot/cold amount, QgtThe hot/cold processed provided for miniature gas turbine high-temperature tail gas waste heat
Amount；
(2) energy storage device SOC value initial time is identical with finish time
Because supply of cooling, heating and electrical powers type micro-capacitance sensor Optimized Operation is presented periodically, therefore, include battery and storage for energy storage device
Initial and end time of the state of hot/cold groove within dispatching cycle should be consistent, i.e.,：
SOCstart_b=SOCend_b (15)
SOCstart_q=SOCend_q (16)
In formula, SOCstart_b、SOCend_b、SOCstart_qAnd SOCend_qBattery initial time and termination respectively in dispatching cycle
The state-of-charge at moment, and accumulation of heat/cold trap initial time and the heat accumulation level for terminating the time；
Constrained for device model, battery power, the startup power and peak power of gas turbine powered generator, it is hard using constraint
The method of processing, the operation variable pressure assignment that will run counter to the element of constraint are boundary value；Battery i.e. such as formula (9) constrains
It is shown：
If | SOCi+1-SOCi| ＞ δ (17)
Then
The constraint of power-balance, typically uses dimension-reduction treatment method in being constrained for system operation, that is, assumes there is N number of change in equation
Amount, wherein N-1 variable is chosen as independent variable；A remaining variable is dependent variable, its value by other independents variable value and
Constraint equation determines jointly；By Pgrid(t) it is used as dependent variable；
For the energy-storage units in the system operation cycle, initial and end time SOC need to be consistent this constraint and its equipment
Model constrains bind lines into the mixed constraints with time coupling；Using flexible constraint processing method, formula will be run counter to
(15) constraint with (16) adds total operating cost as penalty term, so as to form new object function：
F'(X)=F (X)+β | SOCi+1-SOCi| (19)
In formula, β is the SOC constraint penalty factors；F (X) is multiple objective function, i.e., penalty term is incorporated into each specific item scalar functions
On；
Step 3, using the multi-objective particle instructed based on " outstanding particle " the Optimal Operation Model is asked
Solution；Comprise the following steps that：
(1) operating cost and the minimum mesh of Environmental costs using single objective genetic algorithm to supply of cooling, heating and electrical powers type micro-grid system
Mark optimizes scheduling respectively, and preserves Optimized Operation result；
(2) population initializes
Multi-objective particle is initialized, including the number of determination population, total iterations, inertia weight value
Setting and Studying factors；And two scheduling results for preserving genetic algorithm in step (1) are assigned to particle cluster algorithm population at random
In two individuals；
(3) fitness value of each particle is calculated
Fitness function is determined, and calculates fitness value corresponding to each particle；It is true according to the dominance relation of each particle in population
Determine the non-domination solution of population, and non-domination solution is put into external archive collection；
(4) sorted according to crowding distance, and delete the particle beyond scale
(5) the individual extremely optimal and global optimum position of more new particle
The personal best particle and globally optimal solution of more new particle；Determine the method and single goal particle cluster algorithm of globally optimal solution
There is very big difference, when the optimal selection of target is determined by Pareto dominance relations, external archive collection stores
The non-domination solution found, but do not have but in whole set " absolute " optimal solution；From the outside shelves by crowding distance sequence
Case concentrates before selection 10 particle, then randomly selects globally optimal solution of one of particle as this iteration；
(6) speed of more new particle and position
Particle swarm optimization algorithm is derived from the research of flock of birds predation, and the bird in population is abstracted into grain one by one by the algorithm
Son, by the information sharing between these particles with cooperating, follow the particle individual optimal value and population searched
Global optimum update the position of each particle, by successive ignition finally determine global optimum；When algorithm is carried out,
PbestiThe optimal optimal value for referring to a particle and finding so far；GbestRefer to that whole population is found optimal so far
Value；The renewal speed of all particles and location formula are in population：
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<mi>X</mi>
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<mn>1</mn>
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<mi>k</mi>
</msubsup>
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<mo>+</mo>
<mn>1</mn>
</mrow>
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<mrow>
<mo>(</mo>
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</mrow>
In formula, k is iterations；For particle i flying speed；The speed flown for particle i in+1 iteration of kth；
c1、c2For Studying factors, value 2；r1、r2It is the random number between [0,1]；ω is inertia weight coefficient, for weighing
Local optimum and the search capability of global optimum；ω usually requires dynamically to adjust in algorithm, by the iterations line in formula (22)
Function that property is successively decreased calculates inertia weight ω：
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<mrow>
<mi>m</mi>
<mi>a</mi>
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<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>&omega;</mi>
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<mi>n</mi>
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</mrow>
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<mi>k</mi>
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</msub>
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<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>22</mn>
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In formula, ωmaxGeneral value 0.9；ωminFor 0.4；K is current iterations；kmaxFor the maximum iteration of setting；
Speed and the position of whole population particle are updated according to formula (20) and (21)；The speed of particle and position are possible to after renewal
Beyond given search space scope, the particle beyond search space is now assigned to boundary value and by its velocity reversal；In order to
Prevent particle to be absorbed in local optimum, position disturbance is carried out after being chosen to the particle in population according to certain probability；Probable value
Selection should reduce with the increase of iterations, in order to the Fast Convergent in Evolution of Population later stage；
(7) whether end condition is met
Whether evaluation algorithm meets end condition, exports correlated results if meeting, otherwise goes to step (3) and continue executing with；
Between step 4, the result that finally calculates of output, the i.e. total operating cost of system and environmental improvement cost the two targets
Pareto forward positions；Obtain on this basis miniature gas turbine, battery, accumulation of heat/cold trap, air-conditioning per period output and
Electrical power is interacted with bulk power grid.
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