CN104537435B - Distributed power source Optimal Configuration Method based on user side economic index - Google Patents
Distributed power source Optimal Configuration Method based on user side economic index Download PDFInfo
- Publication number
- CN104537435B CN104537435B CN201410801949.1A CN201410801949A CN104537435B CN 104537435 B CN104537435 B CN 104537435B CN 201410801949 A CN201410801949 A CN 201410801949A CN 104537435 B CN104537435 B CN 104537435B
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- represent
- represented
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000007935 neutral effect Effects 0.000 claims abstract description 18
- 239000007789 gas Substances 0.000 claims description 78
- 238000005070 sampling Methods 0.000 claims description 66
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 56
- 238000005338 heat storage Methods 0.000 claims description 49
- 230000005611 electricity Effects 0.000 claims description 41
- 238000004519 manufacturing process Methods 0.000 claims description 25
- 238000004146 energy storage Methods 0.000 claims description 23
- 238000009434 installation Methods 0.000 claims description 21
- 238000009825 accumulation Methods 0.000 claims description 18
- 238000010438 heat treatment Methods 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 8
- 238000007599 discharging Methods 0.000 claims description 6
- 238000002485 combustion reaction Methods 0.000 claims description 5
- 239000000446 fuel Substances 0.000 claims description 5
- 230000002068 genetic effect Effects 0.000 claims description 5
- 239000003345 natural gas Substances 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 5
- 239000000567 combustion gas Substances 0.000 claims description 3
- 238000010348 incorporation Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims 1
- 238000005457 optimization Methods 0.000 abstract description 10
- 238000004458 analytical method Methods 0.000 abstract description 8
- 230000008901 benefit Effects 0.000 abstract description 5
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000011234 economic evaluation Methods 0.000 abstract description 3
- 230000001737 promoting effect Effects 0.000 abstract description 3
- 238000010977 unit operation Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 8
- 238000011160 research Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 210000000349 chromosome Anatomy 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 239000002918 waste heat Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a kind of distributed power source Optimal Configuration Method based on user side economic index, it is related to power system distributed generation technology field.This method is first with the minimum target of user cost, with reference to each unit operation constraints, establish the mathematical modeling of optimization problem, obtain the income and cost of user side and grid side, change the capacity of each distributed power source again and power is brought into Optimized model and calculated, corresponding income and cost are obtained, and neutral net is trained in this, as input and output, so as to obtain optimal distributed power source capacity and power.Advantage is：Give the economic analysis and appraisal procedure of the Grid-connected Distributed Generation Power System based on user side economic index, obtain the allocation optimum for making user make a profit maximum, economic evaluation has also been carried out to supply side on this basis, the optimization problem of system is reduced to linear programming problem, simplify calculating, give comparison comprehensively and system Economic Analysis Method, have engineering promotional value.
Description
Technical field
The invention belongs to power system distributed generation technology field, and in particular to one kind is based on user side economic index
Distributed power source Optimal Configuration Method.
Background technology
With becoming increasingly conspicuous for global environment and energy problem, environment-friendly and economic distributed power generation more obtains
Concern.The system combined by distributed power source and energy storage device, while distributed power source generates electricity, due to energy storage device
Addition, make system energy flow control it is more flexible, can according to used in the output electric energy of distributed power source and user electricity price
Situation controls the type of flow of energy, and such system can reduce terminal user's expense, improve the quality of power supply, safeguard power network
Stable operation, while have it is environment-friendly, regenerate sustainable use the advantages of, be Future New Energy Source development with popularization it is important
Direction.Because different distributed power sources have different qualities, the scope that it is applicable is also different, and user need to be according to place
Region and environmental quality, suitable distributed power source is selected to access power network.At following 10 years, intelligent residential district in city with it is small-sized
The small-scale distributed power source that enterprise is best suitable for using is photovoltaic generation, miniature gas turbine.
In user side, power network is accessed for distributed power source, if can be the problem of user brings interests to be first concern,
So the Optimal Configuration Method of set of system and the convenient distributed power source calculated is needed to help to assess and analysis distribution formula electricity
The economy and reliability in source.The research in terms of distributed power source is concentrated mainly on both at home and abroad at present the economic load dispatching of system with
In terms of the addressing constant volume of power supply, lack a set of comparison system and complete economic analysis and evaluation scheme；In optimized algorithm side
Face, mostly by for distributed power source, energy storage device founding mathematical models, with the gross investment of system at least for object function, with
The reliability service of power network is constraints, and to determine dispatching method and constant volume, but the elder generations of algorithms are more focused in these researchs
The property entered, causes to calculate that cumbersome and result confidence level is not high, and engineering practice is not strong, and promotional value is not high.
Therefore, establish a set of fairly perfect, and calculate simple, be easy to the optimization of distributed power source promoted in engineering
Collocation method, it is very urgent for future world greatly develops new energy.
For the mathematical modeling of photovoltaic and miniature gas turbine, can be determined according to existing method.
1) photovoltaic array：
PPV=η * S*R* [1-0.005 (T-25)]
S=PPVm/PPVm1
Wherein, PPVFor the power output of photovoltaic array, η is the specified conversion efficiency of photovoltaic, and S is the area of photovoltaic array
(m2), R is the solar irradiation intensity (W/m in photovoltaic module inclined plane2), T is photovoltaic module temperature (degree Celsius)；PPVm1To be every
The peak power output of square metre photovoltaic (see manufacturer's technical parameter)；PPVmThe maximum work output of the photovoltaic array used for user
Rate.
Thus model is visible, simply enters the specified efficiency eta of photovoltaic, the area S of photovoltaic array, and photovoltaic module tilts
Solar irradiation intensity R on face, photovoltaic module temperature T, it is possible to obtain the power output of photovoltaic array.For giving model
Photovoltaic module, its performance parameter-photovoltaic peak power PPVmIt is directly proportional to area S.(YonaA, Senjyu T, Funabashi
T.Application ofrecurrent neural networkto short-term-ahead generatingpower
forecasting for photovoltaic system[C].IEEE Power Engineering Society General
Meeting, 2007).
2) miniature gas turbine
The coefficients such as the miniature gas turbine for giving model, efficiency, specific heat capacity, loss coefficient, regenerator effectiveness, expansion ratio,
It can be obtained from the technical parameter of gas turbine, or exemplary reference value obtains from engineering, in order to simplify gas turbine mould
Type is easy to calculate, compressor inlet air pressure P1, entering air temperature T1, inlet air flow rate Gk, the delivery temperature of regenerator
T4aExemplary reference value can be taken.So it need to only input entry of combustion chamber fuel flow rate Gf, it is possible to obtain the gas turbine
Power output PiAnd caused heat Q (t)i(t).In other words, gas turbine model is given, then its generating efficiency is also just true
It is fixed, put into a certain amount of fuel to combustion chamber, what the output of electricity and heat was just to determine, and can be by following linear relationship table
Show：
Pi(t)=m × Gf(t)
Wherein Gf(t) it is entry of combustion chamber fuel flow rate；Pi(t) it is the power output of gas turbine, Qi(t) it is combustion gas wheel
The output heat energy of machine, m, n are constant (depending on miniature gas turbine model).(Ma Wanling, it is miniature in distributed energy resource system
The research [D] of gas turbine cogeneration of heat and power, HeFei University of Technology, 2008.3 (16-26))
Neutral net is a kind of algorithm number for imitating animal nerve network behavior feature, carrying out distributed parallel information processing
Learn model.This network relies on the complexity of system, by adjusting the relation being connected with each other between internal great deal of nodes, so as to
Reach the purpose of processing information.Neutral net is trained with sample, that is, sets the structure of network, the tax of each neuron is lived letter
Number, and the training parameter such as function and learning rate, after initializing network, are input to neutral net with sample input value, obtain phase
After the output answered compared with the output valve of sample, according to error come corrective networks, by certain number and the instruction of a period of time
Practice, error reaches i.e. training in preset range and finished, and the neutral net obtained with this can input in very accurate analog sample
Relation between output.
Genetic algorithm is according to the mathematical modeling of nature biotechnology chromosome evolution, and it is the gene for imitating chromosome certainly
Selected, intersected and made a variation during so evolving generation population of future generation.Neutral net optimal value is searched with genetic algorithm
The step of operation is:The dimension and object function of input variable are determined, gives the span of input variable, determines that population is advised
Mould, maximum genetic algebra, optimization aim precision, and the parameter such as selection crossover operator.The calculating of software can thus be passed through
Export and inputted corresponding to the optimal value and optimal value of object function.
The content of the invention
The invention provides a kind of distributed power source Optimal Configuration Method based on user side economic index, this method are first
First with the minimum target of user cost, with reference to each unit operation constraints, the mathematical modeling of optimization problem is established, obtains user
Side and the income and cost of grid side, then change the capacity of each distributed power source and power is brought into Optimized model and calculated, obtain
Corresponding income and cost, and neutral net is trained in this, as input and output, so as to obtain optimal distributed power source capacity
And power.Advantage is：System optimization scheduling model is constructed, gives the distributed power source based on user side economic index simultaneously
The economic analysis and appraisal procedure of net system, and the allocation optimum for making user make a profit maximum is obtained, while on this basis
Also economic evaluation has been carried out for supply side.The optimization problem of system is wherein reduced to linear programming problem, enormously simplify
Calculate, and give comparison comprehensively and system Economic Analysis Method, have engineering promotional value.
In order to achieve the above object, the present invention adopts the following technical scheme that：
1st, a kind of distributed power source Optimal Configuration Method based on user side economic index, it is characterised in that this method
Comprise the steps of：
A, user is determined, photovoltaic, miniature gas cogeneration units, battery and water storage as used in determining user
The specification and model of case, the index listed by table 1 are the value of determination,
The input quantity of table 1
Cogeneration of heat and power operational mode, photovoltaic peak power P are determined againPVm, miniature gas turbine installation number N, battery appearance
AmountWith heat storage water tank capacity Htotal；
B, establish using user cost be minimised as the mathematical modeling of target as：
Object function minf=fgas+fgrid+fa-Ql+fa-Qs+fa-Ps+fP-loss+fQ-loss
Wherein：F represents user's operating cost of 24 hours, fgasIt is natural to represent that miniature gas turbine is consumed for 24 hours
Gas cost, fgridRepresent user's electricity charge paid to power network of 24 hours, fa-Ql24 hours punishment costs for abandoning thermic load are represented,
fa-QsRepresent the unnecessary punishment cost of the heat production of 24 hours, fa-PsRepresent the unnecessary punishment cost of the electricity production of 24 hours, fP-lossIt is
The punishment cost of battery discharge and recharge simultaneously, fQ-lossIt is the punishment cost of heat storage water tank heat supply simultaneously and heat accumulation；
Wherein CnRepresent the unit price of natural gas；N represents the gas turbine quantity of installation, Gf(k) k-th of sampling instant is represented
The air inflow of gas turbine, Δ T are the sampling time；
Wherein Cgrid(k) represent k-th of sampling instant power network to the electricity price of user's sale of electricity, Pgrid(k) k-th of sampling is represented
The electric energy that moment power network provides a user, Δ T are the sampling time；
Wherein Ca-QlThermic load penalty factor, Q are abandoned in expressionabanl(k) represent that thermic load amount is abandoned in k-th of sampling instant, Δ T is
Sampling time；
Wherein Ca-QsRepresent the unnecessary penalty factor of heat production, Qabans(k) k-th of sampling instant heat production margin is represented, Δ T is
Sampling time；
Wherein Ca-PsRepresent to produce electricity unnecessary penalty factor, Pabans(k) k-th of sampling instant electricity production margin is represented, Δ T is
Sampling time；
Wherein CP-lossRepresent the penalty factor of battery discharge and recharge simultaneously artificially set, Pba-dis(k) when representing k-th
Carve the discharge power of battery, Pba-ch(k) charge power of k-th of moment battery, η are represented1Represent the charging effect of battery
Rate, η2The discharging efficiency of battery is represented, Δ T is the sampling time；
Wherein CP-loss=2*Ca-Ps, due to Pba-dis(k),Pba-ch(k) battery discharging power and charge power are used as, must
P must be met at momentba-dis(k)*Pba-ch(k)=0, directly calculated if this constraints is put into model, this is planned to one
Non-Linear Programming, calculating can be extremely complex, and obtained solution does not have reliability yet, but is set up if this problem be converted into
CP-loss=2*Ca-Ps, then producing electricity margin can directly bleed off, and will not be disappeared by way of battery simultaneously discharge and recharge off-energy
Consumption, also ensures that Pba-dis(k)*Pba-ch(k)=0.
Wherein CQ-lossRepresent heat storage water tank heat accumulation and the penalty factor of heat supply, and C simultaneously artificially setQ-loss=2*
Ca-Qs；Qtank-sup(k) heating power of k-th of moment heat storage water tank, Q are representedtank-sto(k) k-th of moment heat storage water tank is represented
Heat accumulation power, η3Represent the heat accumulation efficiency of heat storage water tank, η4The heating efficiency of heat storage water tank is represented, Δ T is the sampling time；Its
Middle CQ-loss=2*Ca-Qs.Due to Qtank-supAnd Q (k)tank-sto(k) heating power and heat accumulation power as heat storage water tank, it is necessary to
Moment meets Qtank-sup(k)*Qtank-sto(k)=0, directly calculated if this constraints is put into model, this is planned to one
Individual Non-Linear Programming, calculating can be extremely complex, and obtained solution does not have reliability yet, but is set if this problem be converted into
Vertical CQ-loss=2*Ca-Qs, then heat production margin can directly bleed off, will not pass through heat storage water tank simultaneously heat accumulation and this loss of heat supply
The mode of energy consumes, it is possible to ensures Qtank-sup(k)*Qtank-sto(k)=0.
Constraints one
Electric energy balance constrains PPV(k)+Pgrid(k)+Pba-dis(k)-Pba-ch(k)+N*Pi(k)=Pl(k)+Pabans(k)
Wherein PPV(k) the output electric energy of k-th of moment photovoltaic array prediction, P are representedgrid(k) k-th of moment power network is represented
The electric energy supplied to user, Pba-dis(k) discharge power of k-th of moment battery, P are representedba-ch(k) represent that k-th of moment stores
The charge power of battery, N represent the gas turbine quantity of user installation, Pi(k) the defeated of k-th of moment single gas turbine is represented
Go out power, Pl(k) the prediction power load of k-th of moment user, P are representedabans(k) k-th of moment electricity production margin is represented；
Wherein Pi(k)=m*Gf(k), Gf(k) it is gas turbine inlet fuel flow rate, m is true by miniature gas turbine model
Fixed constant；
Constraints two
Thermal energy balance constrains N*Qi(k)-Qabans(k)+Qtank-sup(k)-Qtank-sto(k)=Ql(k)-Qabanl(k)
Wherein Qi(k) the output heat energy of k-th of moment single gas turbine, Q are representedabans(k) k-th of moment heat production is represented
Margin, Qtank-sup(k) heating power of k-th of moment heat storage water tank, Q are representedtank-sto(k) k-th of moment water storage is represented
The heat accumulation power of case, Ql(k) the prediction thermic load of k-th of moment user, Q are representedabanl(k) represent that k-th of moment abandons thermic load
Amount, N represent the gas turbine quantity of installation；
WhereinPi(k) power output of k-th of moment single gas turbine is represented；M, n are by micro-
The constant that type gas turbine model determines；
Constraints three
Miniature gas turbine operation constraint
Pi min≤Pi(k)≤Pi max
Wherein Pi minAnd Pi maxThe minimum and maximum power limit of gas turbine operation, P are represented respectivelyi(k) represent k-th
Moment is single
The power output of individual gas turbine,Gas turbine creep speed limitation up and down is represented respectively；
Constraints four
Battery operation constraint
Es(k)=Es(k-1)+Pba-ch(k)*η1*ΔT-Pba-dis(k)/η2*ΔT
0≤Pba-ch(k)≤Ps max
0≤Pba-dis(k)≤Ps max
Wherein Es(k) dump energy of k-th of moment battery, P are representedba-dis(k) k-th moment battery is represented
Discharge power, Pba-ch(k) charge power of k-th of moment battery, P are representeds maxRepresent the peak power limit of battery operation
System, η1Represent the charge efficiency of battery, η2The discharging efficiency of battery is represented,The maximum energy storage of battery is represented,
SOCminAnd SOCmaxThe minimum and maximum energy storage state of battery is represented respectively,Represent the initial energy storage state of battery；
Constraints five
Heat storage water tank operation constraint
H (k)=H (k-1)+Qtank-sto(k)*η3*ΔT-Qtank-sup(k)/η4*ΔT
Htotal*SOTmin≤H(k)≤Htotal*SOTmax
H0=SOTmin*Htotal
Wherein H (k) represents the dump energy of k-th of moment heat storage water tank, Qtank-sup(k) k-th of moment water storage is represented
The heating power of case, Qtank-sto(k) the heat accumulation power of k-th of moment heat storage water tank is represented,WithHeat accumulation is represented respectively
Water tank minimum and maximum runs power, HtotalRepresent the maximum stored energy capacitance of heat storage water tank, SOTminAnd SOTmaxStorage is represented respectively
The minimum and maximum energy storage state of boiler, H0Represent the initial energy storage state of heat storage water tank；
Constraints six
Power network supply electric energy constraint
Wherein Pgrid(k) electric energy that k-th of moment power network is supplied to user is represented,Represent the maximum work of power network supply
Rate limits；
Constraints seven
Miniature gas turbine is grid-connected two kinds of operational modes：Electricity determining by heat pattern and unlimited heating power mode.Wherein for
The electricity determining by heat pattern of grid-connected cogeneration of heat and power has Qabanl(k)=0
Wherein Qabanl(k) represent that k-th of moment abandons thermic load amount；
The object function for meeting above-mentioned 7 constraints is solved, can obtain the daily Optimized Operation scheme of system；
Determine cogeneration of heat and power operational mode, photovoltaic peak power PPVm, miniature gas turbine installation number N, accumulator capacityWith heat storage water tank capacity Htotal, solve and meet the object function of above-mentioned 7 constraints, can obtain daily excellent of system
Change scheduling scheme；
C, the income f of user side1Calculation formula it is as follows：
Wherein f1Represent user's income in 1 year, Pl(k) the prediction power load of k-th of sampling instant user is represented,
CpvsubRepresent the subsidy that photovoltaic often generates a kilowatt, Ppv(k) generated output of photovoltaic, C are representedgrid(k) k-th of sampling instant is represented
Price of the power network to user's sale of electricity；fYgasRepresent the gas cost that miniature gas turbine is consumed in 1 year；fYgridRepresent one
The electricity charge that user pays to power network in year；fYa-QlThe punishment cost of thermic load is abandoned in expression in 1 year；fYa-QsRepresent heat production in 1 year
Unnecessary punishment cost；fYa-PsRepresent to produce electricity unnecessary punishment cost in 1 year；Δ T is the sampling time；
Wherein CnRepresent the unit price of natural gas；N represents the gas turbine quantity of installation, Gf(k) k-th of sampling instant is represented
The air inflow of gas turbine, Δ T are the sampling time；
Wherein Cgrid(k) represent k-th of sampling instant power network to the price of user's sale of electricity, Pgrid(k) k-th of sampling is represented
The electric energy that moment power network provides a user, Δ T are the sampling time；
Wherein Ca-QlThermic load penalty factor, Q are abandoned in expressionabanl(k) represent that thermic load amount is abandoned in k-th of sampling instant, Δ T is
Sampling time；
Wherein Ca-QsRepresent the unnecessary penalty factor of heat production, Qabans(k) k-th of sampling instant heat production margin is represented, Δ T is
Sampling time；
Wherein Ca-PsRepresent to produce electricity unnecessary penalty factor, Pabans(k) k-th of sampling instant electricity production margin is represented, Δ T is
Sampling time；
D, user side investment is cost f2Calculation formula it is as follows：
f2=fPV/nPV+fgas-turbine/ni+ftank/ntank+fbattery/nba
Wherein f2Expression is converted into the customer investment of 1 year；fpv=Cpv*PpvmRepresent the installation price of photovoltaic array；CpvTable
Show photovoltaic per square meter price, PpvmRepresent the dressing amount of photovoltaic, nPVRepresent the service life of photovoltaic, fgas-turbine=Ci* N is represented
The installation price of miniature gas turbine, CiThe price of gas turbine is represented, N represents the number of units of miniature gas turbine；niRepresent miniature
The service life of gas turbine, ftank=Ctank*HtotalRepresent the installation price of heat storage water tank, CtankRepresent the list of heat storage water tank
Valency, HtotalRepresent the capacity of heat storage water tank, ntankThe service life of heat storage water tank is represented,Represent electric power storage
The installation price in pond, CbaThe unit price of battery is represented,Represent the capacity of battery, nbaRepresent the service life of battery；
E, grid side income f3Calculation formula it is as follows：
Wherein f3Power network income is represented,Represent the difference of the peak load of the grid-connected front and rear power network of distributed power source, Pl max
The grid-connected preceding peak load of distributed power source is represented,Peak load after expression expression distributed power source is grid-connected, CtranRepresent
The investment of every kilovolt-ampere of transformer and maintenance cost；
F, grid side income decrement is cost f4Calculation formula it is as follows：
Wherein f4Represent power network income decrement, Cplant-grid(k) thermal power plant's rate for incorporation into the power network, C are representedgrid(k) kth is represented
Individual sampling instant power network is to the electricity price of user's sale of electricity, Pl(k) the prediction power load of k-th of moment user, P are representedgrid(k) table
Show the electric energy that k-th of sampling instant power network provides a user, k is sampling instant, and Δ T is the sampling time；
When distributed power source combines：Photovoltaic peak power PPVm, miniature gas turbine installation number N, accumulator capacityHeat storage water tank capacity HtotalAfter it is determined that, the income and cost f1, f2, f3, f4 of available user side and grid side；
According to the following steps, using the capacity and power combination of different distributed power sources：
Wherein PPVmThe upper limitPl maxFor the maximum of user power utilization load；
0≤N≤Nmax
Wherein N upper limit Nmax=[Pl max/Pi max], Pi maxFor the peak power output of miniature gas turbine, [] expression pair
Numerical value inside bracket rounds；
WhereinThe upper limitPl(k) it is the prediction of k-th of moment user
Power load, Δ T are the sampling time；
0≤Htotal≤Htotalmax
The upper limit thereinQl(k) born for the pre- calorimetric of k-th of moment user
Lotus, Δ T are the sampling time；
Change P according to the following stepsPVm,N,HtotalValue, obtain corresponding user side and grid side income and
Cost f1, f2, f3, f4；
Step1.k=1；
Step2.i1=1；
Step3.i2=1；
Step4.i3=1；
Step5.i4=1；
Step6.
N (k)=i2；
Htotal(k)=(i4-1) * Htotalmax/20；
K=k+1；
By PPVm(k),N(k),Htotal(k) substitute into step b-f, obtain corresponding f1 (k), f2 (k), f3
(k), f4 (k) value；
Step7.i4=i4+1；
If i4≤21, return to step6；
Step8.i3=i3+1；
If i3≤21, return to step5；
Step9.i2=i2+1；
If i2≤Nmax, then step4 is returned；
Step10.i1=i1+1；
If i1≤21, return to step3；
Derived above 214*NmaxGroup (PPVm,N,Htotal, f1, f2, f3, f4) and it is vectorial as neutral net
Training sample trains neutral net, and obtained neutral net can represent input value (PPVm,N,Htotal) and output valve
The relation of (f1, f2, f3, f4)；
The globally optimal solution of neutral net is searched by genetic algorithm, so as to obtain optimal distributed power source capacity and work(
Rate.
This method with the minimum target of user cost, with reference to each unit operation constraints, establishes optimization problem first
Mathematical modeling, obtain the income and cost of user side and grid side, then change the capacity of each distributed power source and power bring into it is excellent
Change and calculated in model, obtain corresponding income and cost, and neutral net is trained in this, as input and output, it is optimal so as to obtain
Distributed power source capacity and power.
The technical effects of the invention are that：System optimization scheduling model is constructed, gives and is referred to based on user side economy
The economic analysis and appraisal procedure of target Grid-connected Distributed Generation Power System, and obtain optimal the matching somebody with somebody for making user's profit maximum
Put, while also carried out economic evaluation for supply side on this basis.The optimization problem of system is wherein reduced to linear gauge
The problem of drawing, enormously simplify calculatings, and give that comparison is comprehensive and the Economic Analysis Method of system, great engineering promotion price
Value.
Brief description of the drawings
Fig. 1 is the schematic diagram of present system structure.
Fig. 2 is the schematic diagram of inventive algorithm flow.
Embodiment
In order to more specifically describe the present invention, below in conjunction with the accompanying drawings, with the grid-connected system of the distributed power source of certain industry and commerce user
Exemplified by system, grid-connected distributed power supply system structured flowchart is as shown in figure 1, being analyzed and being assessed to its economy, and obtained most
Excellent configuration, is comprised the following steps that：.
1) mathematical modeling and resource data of distributed power source are determined
For photovoltaic generation, this area the photometric data R and temperature data T of 1 year are obtained；Photovoltaic cost：Cpv=
10yuan/w, conversion ratio η=10%, per square meter photovoltaic peak power output PPVm1=0.07 life-span npvAbout 20 years.
For miniature gas turbine, parameter is determined according to actual conditions, obtains air inflow and generated output and heat production power
Between relation, i.e. Pi(t)=m*GfAnd Q (t)i(t)=n*Gf(t), wherein Pi(t) generated output of gas turbine, Q are representedi
(t) heating power of waste heat boiler, G are representedf(t) air inflow of gas turbine is represented, m, n are constant.To Capstone
C65 miniature gas turbines, it can be obtained according to its model：Pi(t)=12057.38986*Gf(t), Qi(t)=
20718.71506*Gf(t), and MT runs the bound P of poweri min=14KW, Pi max=65KW, upper and lower creep speed limitation are full
FootPrice Ci is 500,000 yuan/platform, life-span ni about 20 years.
Battery charge efficiency η1=0.9, discharging efficiency η2=0.8, charge-discharge electric power limitationEnergy storage
State bound SOCmin=0.2, SOCmax=0.8, initial energy storage statePrice CbaFor 500 yuan/
Kw, life-span nbaAbout 2 years.
Heat storage water tank heat accumulation efficiency eta3=0.9, heating efficiency η4=0.8, heat accumulation limits with heating powerWater tank energy storage state bound SOTmin=0.2, SOTmax=0.8, initial energy storage state H0=SOTmin*
Htotal.Price CtankFor 21.5/kw, life-span ntankAbout 10 years.
For grid side：Thermal power plant rate for incorporation into the power network Cplant-grid=0.3 yuan, transformer capacity invests in maintenance cost Ctran
=600yuan/kVA.
Obtain 1 year prediction electric load P of userlWith prediction thermic load data Ql。
2) systematic parameter and cogeneration of heat and power pattern are determined
For some specific system, its user side parameter：Sampling time Δ T=1h, Gas Prices Cn=
2.718yuan/kg tou power price CgridAs shown in table 1, thermic load penalty factor is abandoneda-Ql=0.46, the unnecessary penalty factor of heat production
Ca-Qs=0.25, produce electricity unnecessary penalty factora-ps=0.5, the penalty factor of heat storage water tank heat accumulation and heat supply simultaneouslyP-loss=2*
Ca-ps=1.The penalty factor of energy storage device discharge and recharge simultaneouslyQ-loss=2*Ca-Qs=0.5.
Cogeneration of heat and power is with unlimited heating power mode operation.
The tou power price of table 1
| Period (hour) | 01-08 | 09-10 | 11-12 | 13-18 | 19-22 |
| Electricity price (member) | 0.2616 | 0.5450 | 0.981 | 0.5450 | 0.981 |
3) daily Optimal Operation Model is：
Object function：
Constraints：
PPV(k)+Pgrid(k)+Pba-dis(k)-Pba-ch(k)+N*Pi(k)=Pl(k)+Pabans(k)
N*Qi(k)-Qabans(k)+Qtank-sup(k)-Qtank-sto(k)=Ql(k)-Qabanl(k)
Pi(t)=m × Gf(t)
Es(k)=ES(k-1)+Pba-ch(k)*η1*ΔT-Pba-dis(k)/η2*ΔT
H (k)=H (k-1)+Qtank-sto(k)*η3*ΔT-Qtank-sup(k)/η4*ΔT
Htotal*SOTmin≤H(k)≤Htotal*SOTmax
3) user's income and the investment, and the income of grid side and investment of 1 year is calculated by daily scheduling result：
User's annual earnings：
User's year invests：
f2=fPV/nPV+fgas-turbine/ni+ftank/ntank+fbattery/nba
Grid side annual earnings：
Grid side year invests：
4) (capacity, power) is combined with different distributed power sources：Photovoltaic peak power PPVm, miniature gas turbine installation
Number N, energy-storage battery capacityHeat storage water tank capacity HtotalAs the input of model in step 1, with the user accordingly obtained
Side and the income and cost f1, f2, f3 of grid side, f4 is output to train neutral net, then the neutral net by training to search
Seek globally optimal solution, i.e. optimum power configuration.Algorithm flow chart is as shown in Figure 2.
4) above-mentioned parameter is brought into model and calculated, obtain the optimum combination of distributed power source, and corresponding user receives
Benefit and investment,
Power network income and investment.
The allocation optimum, which is calculated, is：
Photovoltaic peak power PPVm=352kw；
Miniature gas turbine number N=43；
Energy-storage battery capacity ES=870kwh；
Heat storage water tank capacity H=2552kwh.
Corresponding output is (1 year)：
User's income f1=253.43 ten thousand yuan
Customer investment f2=131.56 ten thousand yuan
Power network income f3=229.96 ten thousand yuan
Electric grid investment f4=139.42 ten thousand yuan
Ten thousand yuan of user's net profit f1-f2=121.87
5) economy of allocation optimum combination
Change the configuration of distributed installed capacity, calculate the net profit of user, received only with the user under optimal installed capacity
Benefit is relatively
As photovoltaic peak power PPVm=500kw；
Miniature gas turbine number N=50；
Energy-storage battery capacity ES=870kwh；
Heat storage water tank capacity H=2552kwh.
Corresponding output is (1 year)：
User's income f1=260.21 ten thousand yuan
Customer investment f2=149.80 ten thousand yuan
Now ten thousand yuan of user's net profit f1-f2=116.41
As photovoltaic peak power PPVm=352kw；
Miniature gas turbine number N=50；
Energy-storage battery capacity ES=1000kwh；
Heat storage water tank capacity H=2552kwh.
Corresponding output is (1 year)：
User's income f1=262.87 ten thousand yuan
Customer investment f2=152.31 ten thousand yuan
Now ten thousand yuan of user's net profit f1-f2=110.56
As photovoltaic peak power PPVm=200kw；
Miniature gas turbine number N=50；
Energy-storage battery capacity ES=1000kwh；
Heat storage water tank capacity H=2000kwh.
Corresponding output is (1 year)：
User's income f1=264.87 ten thousand yuan
Customer investment f2=160.44 ten thousand yuan
Now ten thousand yuan of user's net profit f1-f2=104.43
It can be seen that after changing the installed capacity of distributed power source, the net profit of user is than under optimal installed capacity
User's net profit is small, so as to demonstrate its economy.
Claims (1)
1. a kind of distributed power source Optimal Configuration Method based on user side economic index, it is characterised in that this method includes
The following steps：
A, user is determined, photovoltaic, miniature gas cogeneration units, battery and heat storage water tank as used in determining user
Specification and model, then the index listed by table 1 is the value of determination,
The input quantity of table 1
Cogeneration of heat and power operational mode, photovoltaic peak power P are determined againPVm, miniature gas turbine installation number N, accumulator capacityWith heat storage water tank capacity Htotal；
B, establish using user cost be minimised as the mathematical modeling of target as：
Object function minf=fgas+fgrid+fa-Ql+fa-Qs+fa-Ps+fP-loss+fQ-loss
Wherein：F represents user's operating cost of 24 hours, fgasRepresent 24 hours natural gases consumed of miniature gas turbine into
This, fgridRepresent user's electricity charge paid to power network of 24 hours, fa-QlRepresent 24 hours punishment costs for abandoning thermic load, fa-Qs
Represent the unnecessary punishment cost of the heat production of 24 hours, fa-PsRepresent the unnecessary punishment cost of the electricity production of 24 hours, fP-lossIt is electric power storage
The punishment cost of pond discharge and recharge simultaneously, fQ-lossIt is the punishment cost of heat storage water tank heat supply simultaneously and heat accumulation；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>g</mi>
<mi>a</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mi>n</mi>
</msub>
<mo>*</mo>
<mi>N</mi>
<mo>*</mo>
<msub>
<mi>G</mi>
<mi>f</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein CnRepresent the unit price of natural gas；N represents the gas turbine quantity of installation, Gf(k) k-th of sampling instant combustion gas is represented
The air inflow of turbine, Δ T are the sampling time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Cgrid(k) represent k-th of sampling instant power network to the electricity price of user's sale of electricity, Pgrid(k) k-th of sampling instant is represented
The electric energy that power network provides a user, Δ T are the sampling time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>*</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>a</mi>
<mi>n</mi>
<mi>l</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Ca-QlThermic load penalty factor, Q are abandoned in expressionabanl(k) represent that thermic load amount is abandoned in k-th of sampling instant, Δ T is sampling
Time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>*</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>a</mi>
<mi>n</mi>
<mi>s</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Ca-QsRepresent the unnecessary penalty factor of heat production, Qabans(k) k-th of sampling instant heat production margin is represented, Δ T is sampling
Time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>P</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>P</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>a</mi>
<mi>n</mi>
<mi>s</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Ca-PsRepresent to produce electricity unnecessary penalty factor, Pabans(k) k-th of sampling instant electricity production margin is represented, Δ T is sampling
Time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>P</mi>
<mo>-</mo>
<mi>l</mi>
<mi>o</mi>
<mi>s</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<mo>{</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>P</mi>
<mo>-</mo>
<mi>l</mi>
<mi>o</mi>
<mi>s</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>*</mo>
<mo>&lsqb;</mo>
<mrow>
<mo>(</mo>
<mfrac>
<mn>1</mn>
<msub>
<mi>&eta;</mi>
<mn>2</mn>
</msub>
</mfrac>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>b</mi>
<mi>a</mi>
<mo>-</mo>
<mi>d</mi>
<mi>i</mi>
<mi>s</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>&eta;</mi>
<mn>1</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>b</mi>
<mi>a</mi>
<mo>-</mo>
<mi>c</mi>
<mi>h</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
<mo>}</mo>
</mrow>
Wherein CP-lossRepresent the penalty factor of battery discharge and recharge simultaneously artificially set, and CP-loss=2*Ca-Ps；Pba-dis(k)
Represent the discharge power of k-th of moment battery, Pba-ch(k) charge power of k-th of moment battery, η are represented1Represent electric power storage
The charge efficiency in pond, η2The discharging efficiency of battery is represented, Δ T is the sampling time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>Q</mi>
<mo>-</mo>
<mi>l</mi>
<mi>o</mi>
<mi>s</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>24</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<mo>{</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>Q</mi>
<mo>-</mo>
<mi>l</mi>
<mi>o</mi>
<mi>s</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>*</mo>
<mo>&lsqb;</mo>
<mrow>
<mo>(</mo>
<mfrac>
<mn>1</mn>
<msub>
<mi>&eta;</mi>
<mn>4</mn>
</msub>
</mfrac>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
<mo>-</mo>
<mi>sup</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>&eta;</mi>
<mn>3</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
<mo>-</mo>
<mi>s</mi>
<mi>t</mi>
<mi>o</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
<mo>}</mo>
</mrow>
Wherein CQ-lossRepresent heat storage water tank heat accumulation and the penalty factor of heat supply, and C simultaneously artificially setQ-loss=2*Ca-Qs；
Qtank-sup(k) heating power of k-th of moment heat storage water tank, Q are representedtank-sto(k) storage of k-th of moment heat storage water tank is represented
Thermal power, η3Represent the heat accumulation efficiency of heat storage water tank, η4The heating efficiency of heat storage water tank is represented, Δ T is the sampling time；
Constraints one
Electric energy balance constrains PPV(k)+Pgrid(k)+Pba-dis(k)-Pba-ch(k)+N*Pi(k)=Pl(k)+Pabans(k)
Wherein PPV(k) the output electric energy of k-th of moment photovoltaic array prediction, P are representedgrid(k) represent k-th of moment power network to
The electric energy of family supply, Pba-dis(k) discharge power of k-th of moment battery, P are representedba-ch(k) k-th of moment battery is represented
Charge power, N represent user installation gas turbine quantity, Pi(k) output work of k-th of moment single gas turbine is represented
Rate, Pl(k) the prediction power load of k-th of moment user, P are representedabans(k) k-th of moment electricity production margin is represented；
Wherein Pi(k)=m*Gf(k), Gf(k) it is gas turbine inlet fuel flow rate, m is determined by miniature gas turbine model
Constant；
Constraints two
Thermal energy balance constrains N*Qi(k)-Qabans(k)+Qtank-sup(k)-Qtank-sto(k)=Ql(k)-Qabanl(k)
Wherein Qi(k) the output heat energy of k-th of moment single gas turbine, Q are representedabans(k) represent that k-th of moment heat production is unnecessary
Amount, Qtank-sup(k) heating power of k-th of moment heat storage water tank, Q are representedtank-sto(k) k-th moment heat storage water tank is represented
Heat accumulation power, Ql(k) the prediction thermic load of k-th of moment user, Q are representedabanl(k) represent that k-th of moment abandons thermic load amount, N
Represent the gas turbine quantity of installation；
WhereinPi(k) power output of k-th of moment single gas turbine is represented；M, n are by miniature combustion
The constant that gas-turbine model determines；
Constraints three
Miniature gas turbine operation constraint
Pi min≤Pi(k)≤Pi max
<mrow>
<mo>-</mo>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>d</mi>
<mi>o</mi>
<mi>w</mi>
<mi>n</mi>
</mrow>
</msubsup>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>u</mi>
<mi>p</mi>
</mrow>
</msubsup>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Pi minAnd Pi maxThe minimum and maximum power limit of gas turbine operation, P are represented respectivelyi(k) k-th of moment is represented
The power output of single gas turbine,Gas turbine creep speed limitation up and down is represented respectively；
Constraints four
Battery operation constraint
Es(k)=Es(k-1)+Pba-ch(k)*η1*ΔT-Pba-dis(k)/η2*ΔT
0≤Pba-ch(k)≤Ps max
0≤Pba-dis(k)≤Ps max
<mrow>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mrow>
<mi>t</mi>
<mi>o</mi>
<mi>t</mi>
<mi>a</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>*</mo>
<msub>
<mi>SOC</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mrow>
<mi>t</mi>
<mi>o</mi>
<mi>t</mi>
<mi>a</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>*</mo>
<msub>
<mi>SOC</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mn>0</mn>
</msubsup>
<mo>=</mo>
<msub>
<mi>SOC</mi>
<mi>min</mi>
</msub>
<mo>*</mo>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mrow>
<mi>t</mi>
<mi>o</mi>
<mi>t</mi>
<mi>a</mi>
<mi>l</mi>
</mrow>
</msubsup>
</mrow>
Wherein Es(k) dump energy of k-th of moment battery, P are representedba-dis(k) the electric discharge work(of k-th of moment battery is represented
Rate, Pba-ch(k) charge power of k-th of moment battery is represented,Represent the peak power limitation of battery operation, η1Table
Show the charge efficiency of battery, η2The discharging efficiency of battery is represented,Represent the maximum energy storage of battery, SOCminWith
SOCmaxThe minimum and maximum energy storage state of battery is represented respectively,Represent the initial energy storage state of battery；
Constraints five
Heat storage water tank operation constraint
H (k)=H (k-1)+Qtank-sto(k)*η3*ΔT-Qtank-sup(k)/η4*ΔT
<mrow>
<msubsup>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
</mrow>
<mi>min</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
<mo>-</mo>
<mi>s</mi>
<mi>t</mi>
<mi>o</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
</mrow>
<mi>max</mi>
</msubsup>
</mrow>
<mrow>
<msubsup>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
</mrow>
<mi>min</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
<mo>-</mo>
<mi>s</mi>
<mi>u</mi>
<mi>p</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>Q</mi>
<mrow>
<mi>tan</mi>
<mi>k</mi>
</mrow>
<mi>max</mi>
</msubsup>
</mrow>
Htotal*SOTmin≤H(k)≤Htotal*SOTmax
H0=SOTmin*Htotal
Wherein H (k) represents the dump energy of k-th of moment heat storage water tank, Qtank-sup(k) k-th moment heat storage water tank is represented
Heating power, Qtank-sto(k) the heat accumulation power of k-th of moment heat storage water tank is represented,WithHeat storage water tank is represented respectively
Minimum and maximum runs power, HtotalRepresent the maximum stored energy capacitance of heat storage water tank, SOTminAnd SOTmaxWater storage is represented respectively
The minimum and maximum energy storage state of case, H0Represent the initial energy storage state of heat storage water tank；
Constraints six
Power network supply electric energy constraint
Wherein Pgrid(k) electric energy that k-th of moment power network is supplied to user is represented,Represent the peak power limit of power network supply
System；
Constraints seven
There is Q for the electricity determining by heat pattern of grid-connected cogeneration of heat and powerabanl(k)=0
Wherein Qabanl(k) represent that k-th of moment abandons thermic load amount；
The object function for meeting above-mentioned 7 constraints is solved, can obtain the daily Optimized Operation scheme of system；
C, the income f of user side1Calculation formula it is as follows：
<mrow>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>8760</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mi>l</mi>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>*</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>+</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>p</mi>
<mi>v</mi>
</mrow>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>*</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>p</mi>
<mi>v</mi>
<mi>s</mi>
<mi>u</mi>
<mi>b</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
<mo>-</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>g</mi>
<mi>a</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>a</mi>
<mo>-</mo>
<mi>P</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein f1Represent user's income in 1 year, Pl(k) the prediction power load of k-th of sampling instant user, C are representedpvsubRepresent
The subsidy that photovoltaic often generates a kilowatt, Ppv(k) generated output of photovoltaic, C are representedgrid(k) represent k-th of sampling instant power network to
The price of family sale of electricity；fYgasRepresent the gas cost that miniature gas turbine is consumed in 1 year；fYgridRepresent user in 1 year
The electricity charge paid to power network；fYa-QlThe punishment cost of thermic load is abandoned in expression in 1 year；fYa-QsRepresent that heat production is unnecessary punishes in 1 year
Penalize cost；fYa-PsRepresent to produce electricity unnecessary punishment cost in 1 year；Δ T is the sampling time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>g</mi>
<mi>a</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>8760</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mi>n</mi>
</msub>
<mo>*</mo>
<mi>N</mi>
<mo>*</mo>
<msub>
<mi>G</mi>
<mi>f</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein CnRepresent the unit price of natural gas；N represents the gas turbine quantity of installation, Gf(k) k-th of sampling instant combustion gas is represented
The air inflow of turbine, Δ T are the sampling time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>8760</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Cgrid(k) represent k-th of sampling instant power network to the price of user's sale of electricity, Pgrid(k) k-th of sampling instant is represented
The electric energy that power network provides a user, Δ T are the sampling time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>8760</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>l</mi>
</mrow>
</msub>
<mo>*</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>a</mi>
<mi>n</mi>
<mi>l</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Ca-QlThermic load penalty factor, Q are abandoned in expressionabanl(k) represent that thermic load amount is abandoned in k-th of sampling instant, Δ T is sampling
Time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>8760</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>Q</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>*</mo>
<msub>
<mi>Q</mi>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>a</mi>
<mi>n</mi>
<mi>s</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Ca-QsRepresent the unnecessary penalty factor of heat production, Qabans(k) k-th of sampling instant heat production margin is represented, Δ T is sampling
Time；
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>Y</mi>
<mi>a</mi>
<mo>-</mo>
<mi>P</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>8760</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>C</mi>
<mrow>
<mi>a</mi>
<mo>-</mo>
<mi>P</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>a</mi>
<mi>n</mi>
<mi>s</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
Wherein Ca-PsRepresent to produce electricity unnecessary penalty factor, Pabans(k) k-th of sampling instant electricity production margin is represented, Δ T is sampling
Time；
D, user side investment is cost f2Calculation formula it is as follows：
f2=fPV/nPV+fgas-turbine/ni+ftank/ntank+fbattery/nba
Wherein f2Expression is converted into the customer investment of 1 year；fpv=Cpv*PpvmRepresent the installation price of photovoltaic array；CpvRepresent light
Price of the volt per square meter, PpvmRepresent the dressing amount of photovoltaic, nPVRepresent the service life of photovoltaic, fgas-turbine=Ci* N is represented micro-
The installation price of type gas turbine, CiThe price of gas turbine is represented, N represents the number of units of miniature gas turbine；niRepresent miniature combustion
The service life of gas-turbine, ftank=Ctank*HtotalRepresent the installation price of heat storage water tank, CtankThe unit price of heat storage water tank is represented,
HtotalRepresent the capacity of heat storage water tank, ntankThe service life of heat storage water tank is represented,Represent battery
Price, C are installedbaThe unit price of battery is represented,Represent the capacity of battery, nbaRepresent the service life of battery；
E, grid side income f3Calculation formula it is as follows：
<mrow>
<msubsup>
<mi>&Delta;P</mi>
<mi>l</mi>
<mi>max</mi>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>P</mi>
<mi>l</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
<mi>max</mi>
</msubsup>
</mrow>
<mrow>
<msub>
<mi>f</mi>
<mn>3</mn>
</msub>
<mo>=</mo>
<msubsup>
<mi>&Delta;P</mi>
<mi>l</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msubsup>
<mo>*</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>t</mi>
<mi>r</mi>
<mi>a</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
Wherein f3Power network income is represented,Represent the difference of the peak load of the grid-connected front and rear power network of distributed power source, Pl maxRepresent
Peak load before distributed power source is grid-connected,Peak load after expression expression distributed power source is grid-connected, CtranRepresent transformation
The investment of every kilovolt-ampere of device and maintenance cost；
F, grid side income decrement is cost f4Calculation formula it is as follows：
<mrow>
<msub>
<mi>f</mi>
<mn>4</mn>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mn>8760</mn>
<mo>/</mo>
<mi>&Delta;</mi>
<mi>T</mi>
</mrow>
</munderover>
<mo>&lsqb;</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>C</mi>
<mrow>
<mi>p</mi>
<mi>l</mi>
<mi>a</mi>
<mi>n</mi>
<mi>t</mi>
<mo>-</mo>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>*</mo>
<mo>&lsqb;</mo>
<msub>
<mi>P</mi>
<mi>l</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
Wherein f4Represent power network income decrement, Cplant-grid(k) thermal power plant's rate for incorporation into the power network, C are representedgrid(k) represent to adopt for k-th
Sample moment power network is to the electricity price of user's sale of electricity, Pl(k) the prediction power load of k-th of moment user, P are representedgrid(k) kth is represented
The electric energy that individual sampling instant power network provides a user, k are sampling instant, and Δ T is the sampling time；
When distributed power source combines：Photovoltaic peak power PPVm, miniature gas turbine installation number N, accumulator capacityHeat accumulation
Water tank volume HtotalAfter it is determined that, the income and cost f1, f2, f3, f4 of available user side and grid side；
According to the following steps, using the capacity and power combination of different distributed power sources：
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>P</mi>
<mi>V</mi>
<mi>m</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>P</mi>
<mi>V</mi>
<mi>m</mi>
</mrow>
<mi>max</mi>
</msubsup>
</mrow>
Wherein PPVmThe upper limitPl maxFor the maximum of user power utilization load；
0≤N≤Nmax
Wherein N upper limit Nmax=[Pl max/Pi max], Pi maxFor the peak power output of miniature gas turbine, [] is represented to bracket
The numerical value of the inside rounds；
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mrow>
<mi>t</mi>
<mi>o</mi>
<mi>t</mi>
<mi>a</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mo>&le;</mo>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mrow>
<mi>t</mi>
<mi>o</mi>
<mi>t</mi>
<mi>a</mi>
<mi>l</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msubsup>
</mrow>
WhereinThe upper limitPl(k) it is the prediction electricity consumption of k-th of moment user
Load, Δ T are the sampling time；
0≤Htotal≤Htotalmax
The upper limit thereinQl(k) it is the prediction thermic load of k-th of moment user,
Δ T is the sampling time；
Change P according to the following stepsPVm,N,HtotalValue, obtain the income and cost of corresponding user side and grid side
f1,f2,f3,f4；
Step1.k=1；
Step2.i1=1；
Step3.i2=1；
Step4.i3=1；
Step5.i4=1；
Step6.
N (k)=i2；
<mrow>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mrow>
<mi>t</mi>
<mi>o</mi>
<mi>t</mi>
<mi>a</mi>
<mi>l</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mn>3</mn>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msubsup>
<mi>E</mi>
<mi>s</mi>
<mrow>
<mi>t</mi>
<mi>o</mi>
<mi>t</mi>
<mi>a</mi>
<mi>l</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msubsup>
<mo>/</mo>
<mn>20</mn>
<mo>;</mo>
</mrow>
Htotal(k)=(i4-1) * Htotalmax/20；
K=k+1；
By PPVm(k),N(k),Htotal(k) substitute into step b-f, obtain corresponding f1 (k), f2 (k), f3 (k), f4
(k) value；
Step7.i4=i4+1；
If i4≤21, return to step6；
Step8.i3=i3+1；
If i3≤21, return to step5；
Step9.i2=i2+1；
If i2≤Nmax, then step4 is returned；
Step10.i1=i1+1；
If i1≤21, return to step3；
Derived above 214*NmaxGroup (PPVm,N,Htotal, f1, f2, f3, f4) and training of the vector as neutral net
Sample trains neutral net, and obtained neutral net can represent input value (PPVm,N,Htotal) and output valve (f1,
F2, f3, f4) relation；
The globally optimal solution of neutral net is searched by genetic algorithm, so as to obtain optimal distributed power source capacity and power.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410801949.1A CN104537435B (en) | 2014-12-18 | 2014-12-18 | Distributed power source Optimal Configuration Method based on user side economic index |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410801949.1A CN104537435B (en) | 2014-12-18 | 2014-12-18 | Distributed power source Optimal Configuration Method based on user side economic index |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104537435A CN104537435A (en) | 2015-04-22 |
| CN104537435B true CN104537435B (en) | 2017-12-15 |
Family
ID=52852954
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410801949.1A Active CN104537435B (en) | 2014-12-18 | 2014-12-18 | Distributed power source Optimal Configuration Method based on user side economic index |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104537435B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105322550B (en) * | 2015-08-28 | 2018-03-16 | 南方电网科学研究院有限责任公司 | A kind of optimization method of household micro-capacitance sensor operation |
| US10387775B2 (en) * | 2015-09-09 | 2019-08-20 | Emerson Process Management Power & Water Solutions, Inc. | Model-based characterization of pressure/load relationship for power plant load control |
| CN105281363B (en) * | 2015-10-12 | 2017-10-31 | 清华大学 | The multipotency source optimization and control method of meter and customer consumption viscosity |
| DE102016203855B4 (en) | 2016-03-09 | 2023-11-02 | Siemens Aktiengesellschaft | Method and device for controlling a technical system using control models |
| CN106447152A (en) * | 2016-06-21 | 2017-02-22 | 中国南方电网有限责任公司电网技术研究中心 | Energy center-based power-gas-heat system co-scheduling method and system |
| CN106505614A (en) * | 2016-11-07 | 2017-03-15 | 国网天津市电力公司 | The photovoltaic generation regulating strategy of user's maximizing the benefits under multi-constraint condition |
| CN107863773B (en) * | 2017-11-24 | 2020-04-24 | 清华-伯克利深圳学院筹备办公室 | Multi-energy-flow energy supply method and system |
| CN108197412B (en) * | 2018-02-05 | 2021-03-26 | 东北大学 | Multi-energy coupling energy management system and optimization method |
| CN108494014A (en) * | 2018-02-08 | 2018-09-04 | 能金云（北京）信息技术有限公司 | A kind of energy mix cogeneration of heat and power economy optimum management method |
| CN109544016B (en) * | 2018-11-27 | 2021-07-06 | 中国长江电力股份有限公司 | Intelligent power grid dispatching system and method based on user demands |
| CN109598429B (en) * | 2018-11-27 | 2021-07-06 | 中国长江电力股份有限公司 | Intelligent dispatching system and dispatching method for island micro-grid |
| CN109347150B (en) * | 2018-11-27 | 2021-07-06 | 中国长江电力股份有限公司 | Distributed power generation dispatching system and dispatching method |
| CN109755949B (en) * | 2019-01-07 | 2021-12-17 | 中国电力科学研究院有限公司 | Optimal distribution method and device for power of combined heat and power energy storage power station |
| CN109787261B (en) * | 2019-03-14 | 2020-12-29 | 国网江苏省电力有限公司镇江供电分公司 | Power grid side and user side energy storage system capacity optimization configuration method |
| CN110793093B (en) * | 2019-10-23 | 2021-05-07 | 国家电网有限公司 | Electric power control method for cogeneration unit comprising electric boiler and heat storage tank |
| CN113708406B (en) * | 2021-08-23 | 2022-09-02 | 国网湖南省电力有限公司 | Inertia control method and processor for distributed energy storage type wind power plant |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102623989A (en) * | 2012-03-28 | 2012-08-01 | 湖南大学 | Method for optimization and configuration of intermittent distributed generation (DG) |
| CN102999792A (en) * | 2012-12-20 | 2013-03-27 | 诸暨市供电局 | Method for comprehensive evaluation of power distribution network optimization allocation |
| CN103151805A (en) * | 2013-03-28 | 2013-06-12 | 武汉大学 | Method for optimizing and configuring power supply of grid-connection-mode microgrid |
| CN103490410A (en) * | 2013-08-30 | 2014-01-01 | 江苏省电力设计院 | Micro-grid planning and capacity allocation method based on multi-objective optimization |
| CN103593711A (en) * | 2013-10-31 | 2014-02-19 | 浙江大学 | Distributed power source optimal configuration method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090119233A1 (en) * | 2007-11-05 | 2009-05-07 | Microsoft Corporation | Power Optimization Through Datacenter Client and Workflow Resource Migration |
| US20100306027A1 (en) * | 2009-06-02 | 2010-12-02 | International Business Machines Corporation | Net-Metering In A Power Distribution System |
-
2014
- 2014-12-18 CN CN201410801949.1A patent/CN104537435B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102623989A (en) * | 2012-03-28 | 2012-08-01 | 湖南大学 | Method for optimization and configuration of intermittent distributed generation (DG) |
| CN102999792A (en) * | 2012-12-20 | 2013-03-27 | 诸暨市供电局 | Method for comprehensive evaluation of power distribution network optimization allocation |
| CN103151805A (en) * | 2013-03-28 | 2013-06-12 | 武汉大学 | Method for optimizing and configuring power supply of grid-connection-mode microgrid |
| CN103490410A (en) * | 2013-08-30 | 2014-01-01 | 江苏省电力设计院 | Micro-grid planning and capacity allocation method based on multi-objective optimization |
| CN103593711A (en) * | 2013-10-31 | 2014-02-19 | 浙江大学 | Distributed power source optimal configuration method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104537435A (en) | 2015-04-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104537435B (en) | Distributed power source Optimal Configuration Method based on user side economic index | |
| Wang et al. | Planning and operation method of the regional integrated energy system considering economy and environment | |
| Wang et al. | Optimal design of integrated energy system considering economics, autonomy and carbon emissions | |
| CN109858759B (en) | Industrial park comprehensive energy balance scheduling method | |
| Sawle et al. | PV-wind hybrid system: A review with case study | |
| CN109103926A (en) | Photovoltaic power generation based on more Radiation Characteristics year meteorology scenes receives capacity calculation method | |
| Henning et al. | A comprehensive model for the German electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies—Part I: Methodology | |
| CN108197768B (en) | Energy system and pipe network layout joint optimization method | |
| CN108537409A (en) | A kind of industrial park power distribution network collaborative planning method considering multiple-energy-source coupled characteristic | |
| CN108206543A (en) | A kind of energy source router and its running optimizatin method based on energy cascade utilization | |
| CN115018230A (en) | Low-carbon robust economic optimization operation method of comprehensive energy system considering emission reduction cost | |
| Luo et al. | Bi-level multi-objective optimization of design and subsidies for standalone hybrid renewable energy systems: A novel approach based on artificial neural network | |
| CN113822496A (en) | Multi-unit thermal power plant heat supply mode and parameter online optimization method | |
| CN104794533B (en) | It is included in the capacity optimal configuration method of the distribution network users photovoltaic plant for the electric vehicle that can network | |
| CN108736507A (en) | A kind of heat storage electric boiler optimization method and device promoting wind-powered electricity generation on-site elimination | |
| CN114154744A (en) | Capacity expansion planning method and device of comprehensive energy system and electronic equipment | |
| Sun et al. | Multi-objective robust optimization of multi-energy microgrid with waste treatment | |
| Song et al. | A fuzzy‐based multi‐objective robust optimization model for a regional hybrid energy system considering uncertainty | |
| CN113592365A (en) | Energy optimization scheduling method and system considering carbon emission and green electricity consumption | |
| Ju et al. | A risk aversion optimal model for microenergy grid low carbon‐oriented operation considering power‐to‐gas and gas storage tank | |
| Lv et al. | Optimal capacity configuration model of power-to-gas equipment in wind-solar sustainable energy systems based on a novel spatiotemporal clustering algorithm: A pathway towards sustainable development | |
| Li et al. | Intraday multi-objective hierarchical coordinated operation of a multi-energy system | |
| La Bella et al. | Data-driven modelling and optimal management of district heating networks | |
| Qin et al. | Low-carbon economic multi-objective dispatch of integrated energy system considering the price fluctuation of natural gas and carbon emission accounting | |
| Zhao et al. | Multi-objective bi-level planning of the integrated energy system considering uncertain user loads and carbon emission during the equipment manufacturing process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |