CN108734350A - A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor - Google Patents

A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor Download PDF

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CN108734350A
CN108734350A CN201810474185.8A CN201810474185A CN108734350A CN 108734350 A CN108734350 A CN 108734350A CN 201810474185 A CN201810474185 A CN 201810474185A CN 108734350 A CN108734350 A CN 108734350A
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capacitance sensor
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殷桂梁
张颖
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Yanshan University
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    • 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • 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 method for solving of the invention discloses a kind of power distribution network independence and combined dispatching containing micro-capacitance sensor, establishes the model of the power distribution network containing micro-capacitance sensor；Establish the object function of each micro-capacitance sensor scheduling, the object function of power distribution network scheduling；Establish micro-capacitance sensor and the independent constraints with combined dispatching of power distribution network；Using PSO Algorithm residents micro-capacitance sensor and power distribution network, thermoelectricity connection type micro-capacitance sensor is solved with Benders decomposition methods.In residents micro-capacitance sensor, demand response is considered, with genetic algorithm optimization load curve.Error is predicted for wind-powered electricity generation, establishes the wind turbine scene method model of three-parameter weibull distribution.Present invention can apply to contain more micro-capacitance sensor economic load dispatching technical fields, under the premise of meeting system constraints, multi-party Interest Main Body is made to be satisfied with.System solution is joined to thermoelectricity using Benders decomposition methods, the privacy of two system information of electric heating is effectively protected, improves the accuracy of calculating.

Description

A kind of independent method for solving with combined dispatching of the power distribution network containing micro-capacitance sensor
Technical field
The present invention relates to integrated energy system economic load dispatching technical fields, and in particular to a kind of power distribution network containing micro-capacitance sensor Independent and combined dispatching method for solving.
Background technology
With power market reform, a large amount of distributed generation resource is built by user or new energy supplier in power distribution network, is changed The powering mode that distributed generation resource ownership returns power grid all is become.Participating in the sale of electricity main body of competition can be divided into three classes, first, electricity Net sale of electricity company of enterprise；Second is that social capital investment increment power distribution network, possesses the sale of electricity company of power distribution network operating license；Third, only Vertical sale of electricity company, does not possess the power distribution network power of management, does not undertake electric service of guaranteeing the minimum.Under new model, " solely by grid company monopolization Buy and solely sell " pattern will gradually turn to " over-bought oversold " and the market structure, each main market players is maximumlly former with number one Then, it merchandises in the market in electricity transaction using electric flux as a kind of commodity, so that electricity market is optimal using lever of price Equilibrium state.
Resource distribution effect is guided to give full play to lever of price, promotes regenerative resource and energy conservation and environmental protection power generation project Health, reasonable, orderly development, along with the network access for being compared to conventional electric power generation, wind light generation is surfed the Internet still in disadvantage Position, so in recent years, state revenue and expenditure can give scene online a certain amount of subsidy.
What house type micro-capacitance sensor referred to is exactly family's micro-capacitance sensor, and the research of family microgrid energy problem of management is actually more The intersection in a field relates generally to intelligent power, demand side management, distributed power generation and micro-capacitance sensor technology.Therefore, having must The independence that be directed to micro-capacitance sensor and power distribution network invents a kind of new scheduling method for solving with combined dispatching.
Invention content
Solving the problems, such as that complicated calculations difficulty is big and workload demand response adjusts the purpose of the present invention is to provide a kind of Mode and meanwhile reach the independent method for solving with combined dispatching of power distribution network containing micro-capacitance sensor of benefits of different parties main body satisfaction.
In order to solve above-mentioned technical problem, the method for the invention includes the following steps：
Step 1, the electricity distribution network model containing micro-capacitance sensor, including the model of power distribution network scheduling, the scheduling of residents micro-capacitance sensor are established Model, thermoelectricity connection type micro-capacitance sensor scheduling model；
Step 2, micro-capacitance sensor and the independent object function with combined dispatching of power distribution network are established；
Step 3, the independent constraints with combined dispatching of the power distribution network containing micro-capacitance sensor is established；
Step 4, the scheduling of PSO Algorithm power distribution network is chosen, genetic algorithm optimization load is selected；Using Benders decomposition methods solve thermoelectricity and join micro-capacitance sensor Optimal Scheduling.
The content of step 1 is as follows：
1-1, power distribution network scheduling model
When power distribution network is dispatched, consideration is exchanged from the purchase of electricity of major network, with the electric energy of two class micro-capacitance sensors and scene consumption feelings Condition；
Newton-Raphson approach Load flow calculation formula is as follows：
(1)-(2) in formula, PG、PMAnd PLIndicate that major network injection is active, the injection of two kinds of micro-capacitance sensor is active respectively and Load active power；GijAnd BijConductance between respectively node i j and susceptance；UiIndicate the voltage of node i；θijIndicate section Phase angle difference between point ij；QG、QL、QMIndicate respectively major network injection is idle, two kinds of micro-capacitance sensor inject idle and load without Work(power；
The uncertainty of wind-powered electricity generation error is described using scene method, the formation of scene method includes scene generation and field Scape reduces two key steps；
Using the range of the model split wind power output of " pre- measuring tank ", wind-powered electricity generation data standardization, section size have been subjected to It is 0.0125.Using the wind-powered electricity generation error curve in each section of Weibull fittings of distribution, combined greatly seemingly with particle cluster algorithm Right method estimates parameter；
Weibull distribution probability density expression formula be
(3) in formula, α is scale parameter；β is form parameter；γ is location parameter；
Weibull distribution likelihood function be
(4) in formula, θ=(α, beta, gamma)；
Weibull distribution likelihood function logarithm be
Scene reduction is to make the scene set before reduction and the probability metrics between the scene subclass finally retained most It is small, i.e.,
(6) in formula, piIndicate the probability that scene i occurs,Indicate the distance between scene i and scene j, J The set being made of the scene finally eliminated during scene reduction；
1-2, residents micro-capacitance sensor models
Load is divided into four classes：Firm demand, interruptible load, adjustable load (power adjustable) and translatable load (make It is adjustable with the period)；
Workload demand response model be：
(7) L in formulatFor the actual load of (1~24) moment t, Lobj,tFor the target load of (1~24) moment t, Lfirm,t、 Ltran,t、Lint,tAnd Ladj,tFirm demand, transferable load, interruptible load and adjustable load are indicated respectively；K (t) is t Moment electricity price.
The operating cost of accumulator includes its charging cost and life consumption cost；
Cch-dis=CG+Cch+Cdis (8)
(8)-(10) in formula, Cch-disIndicate storage hair cost, CGIndicate unit quantity of electricity purchase electricity price, Cch, CdisFor electric power storage The life consumption cost of pond charge and discharge, fuel cell mode are to store CinitBattery buys cost of investment；NSBFor cycle charge-discharge Number；It can be seen that charge and discharge cost (Cch、Cdis) by charge and discharge cost coefficient (lch、ldis) determine, and charge and discharge cost coefficient by Charge and discharge whole story state-of-charge (SOCstart, SOCend), charge-discharge electric power (Pch, Pdis) and charge and discharge impact factor (kch, kdis) It is determined.
1-3, thermoelectricity connection type micro-capacitance sensor mathematical model
Thermoelectricity connection type micro-capacitance sensor includes that miniature gas turbine, gas fired-boiler, wind turbine, photovoltaic, energy-storage system and thermoelectricity are negative Lotus, meter and the electric energy between micro-capacitance sensor and power distribution network exchange the related units limits of benefit and each micro battery, establish with electric heating Income Maximum is the micro-capacitance sensor economic load dispatching model of regulation goal；
The cost model of photovoltaic generation unit is：
F(PPV,t)=aIPPPV,t+GEPPV,t (11)
(11) in formula：
(11)-(12) in formula, a indicates the cost coefficient of photovoltaic installation；GEIndicate photovoltaic operation expense coefficient；IP For the specific investment cost cost of photovoltaic generation unit；N indicates service life, is 20 years；
The cost model of wind turbine power generation unit is：
F(PW,t)=aIPPW,t+GEPW,t (13)
(13) in formula, the same photovoltaic generation unit of meaning of identical variable；
Phase change heat accumulator mathematical model：
When considering the cost of phase change heat accumulator, its operation expense and depreciable cost are only considered：
Cm,TB=Cmm,TBHTB (15)
(14), l is the annual interest fixed rate of interest in (15) formula；Cint,TBIndicate depreciable cost of the phase change heat accumulator within investment cycle； CTBFor the cost of investment of first stage of construction；Cm,TBIndicate operation expense；Cmm,TBFor unit power maintenance cost； HTBFor hot merit Rate；
Gas turbine mathematical model：
The economic mathematical model calculation formula of the cogeneration system of gas turbine is as follows：
Qhe=QMT×ηrec×COPhe (18)
(16)-(19) in formula, CMTFor one day fuel cost of miniature combustion engine；CngFor Gas Prices, 2 yuan/m3；LHVngFor Natural gas low heat value, 9.7kwh/m3；PMT,iFor the net electromotive power output of moment i, kW；ηMT,iFor the unit efficiency of moment i；Formula In, QMTFor combustion turbine exhaustion excess heat, kW；ηlFor gas turbine radiation loss system；QheIt is provided for gas turbine flue gas waste heat Heating capacity, kW；COPheFor heating efficiency；ηrecFor flue gas waste heat recovery efficiency；T0For environment temperature, K；T1、T2It is respectively remaining Heat smoke passes in and out the temperature of shore cold, K, T1、T2Take 573.15K and 423.15K；
Gas fired-boiler mathematical model：
The hot relationship of gas in gas fired-boiler operational process:
(20) in formula, ηBFor the actual operating efficiency of gas fired-boiler, 0.85 is taken；FBFor gas fired-boiler gas consumption, Nm3；Q is natural gas low heat valve, 9.7kW/m3；
The gas control emission cost function of miniature combustion engine is as follows：
(21) in formula, αk、λkThe respectively external discount cost of discharge type k, emission factor, M are discharge type.
The content of step 2 is as follows：
Power distribution network using Utilities Electric Co.'s Income Maximum as object function, with demand charge paid minimum by residents micro-capacitance sensor As object function, thermoelectricity connection type micro-capacitance sensor establishes power distribution network and micro-capacitance sensor Joint economics using maximum of getting a profit as object function Scheduling model is：
(22) in formula：PAL,tFor the total load in scene s；CtFor grid company sale of electricity electricity price；CF1、CF2To exchange power Price；PF1,tAnd PF2,tRespectively electric company t periods and micro-capacitance sensor exchanges power；PTR,tIt it is the electric company t periods to defeated The power of power grid purchase；CPCPAnd CPCWRespectively abandon the punishment electricity price that wind abandons light；ΔPPV,tWith Δ PW,tIt respectively abandons wind and abandons light Power；
Residents micro-capacitance sensor is minimum as target using family's same day total electricity bill expenditure：
(23) C indicates that family microgrid day total electricity bill, total electricity bill are that negative value indicates profit in formula；Cb,tFor power purchase price row to Amount, Pb,tIt is user from the purchase of electricity of power grid, Cch-disFor the operation and maintenance cost of energy-storage system；KiPi,tIndicate distributed generation resource Maintenance cost, it is directly proportional to the output of distributed generation resource；
Thermoelectricity connection type micro-capacitance sensor is maximum as regulation goal to get a profit：
(24) in formula, CP、CHThe price of thermal energy and electric energy in micro-capacitance sensor is indicated respectively；PLoad、QLoadHeat, electricity are indicated respectively Load；F(PMT)、F(QMB)、F(PEB)、F(QTB)、F(PPV) and F (PW) indicate that gas turbine, gas fired-boiler, electric power storage are set respectively The operation expense of standby, thermal storage equipment, photovoltaic and wind turbine；F(PGrid) it is purchases strategies of the micro-capacitance sensor from power distribution network.
The content of step 3 is as follows：
3-1, power distribution network constraints：
(1) power-balance
PTR,t+PF1,t+PF2,t=PAL,t (25)
(25) in formula, PTR,tIt is Utilities Electric Co. from higher level's power grid power purchase power；PF1,tIt is electric company from the micro- electricity of residents Net purchase electrical power；PF2,tIt is Utilities Electric Co. from thermoelectricity connection type micro-capacitance sensor power purchase power；PAL,tBy power distribution network band total load；
(2) power distribution network interacts power limit with micro-capacitance sensor
(26) in formula,The bound that respectively power distribution network is exchanged with microgrid power；
(3) grid branch trend constraint
(27) in formula,The respectively strength of current of circuit m, minimum trend,For t moment line The trend of road m；
(4) when operation of power networks, voltage and current must be in rated range, and network loss is no more than maximum value：
(28)-(29) footmark max indicates that its maximum value, footmark min indicate its minimum value in formula；U, I indicates circuit respectively Voltage and current；
3-2, residents micro-capacitance sensor constraints：
(1) electric equilibrium of residents micro-capacitance sensor is constrained to：
Pgrid1(t)+Ppv1(t)+Pbs1(t)=Lload1(t) (30)
(30) in formula, Pgrid1(t) indicate residents micro-capacitance sensor and power distribution network exchanges power；Ppv1(t) it is family's light to be Lie prostrate generator unit generated energy hourly；Pbs1(t) it is the charge or discharge power of accumulator；Lload1(t) it is to be rung by demand After should adjusting, the load at t (1~24) moment；
When demand response, the constraints that Load Regulation need to meet is：
Pflexmin,i≤Pflex,i≤Pflexmax,i (31)
(31) in formula, Pflex,iIt indicates that load is adjusted, bound is constrained to its power adjustable range；
The constraints that load rejection need to meet is：
Nint≤Nintmax (32)
(32) in formula, NintIndicate interruptible load number, NintmaxFor maximum interruptible load number；
(2) energy storage charge and discharge are constrained to：
SOCmin(t)≤SOC(t)≤SOCmax(t) (33)
Xt∈ { 0,1 }, Yt∈ { 0,1 }, Xt·Yt=0 (34)
SOC (0)=SOC (T) (35)
(33)-(36) in formula, SOC indicates the state-of-charge of battery, Pch-disIndicate the charge-discharge velocity of battery；The energy storage Restricted model is equally applicable to thermoelectricity connection type micro-capacitance sensor；
3-3, the constraint of thermoelectricity connection type micro-capacitance sensor：
Electric equilibrium constrains：
Pgrid(t)+Ppv(t)+Pw(t)+PMT(t)+PEB(t)=PLoad(t) (37)
Thermal balance constrains：
QMT+QTB+QMB=QLoad (38)
(37)-(38) in formula, P indicates that electric energy, Q indicate thermal energy, subscript MT, MB, EB, TB, PV, W, Load, grid difference Indicate gas turbine, gas fired-boiler, electric energy storage device, thermal storage equipment, photovoltaic, wind turbine, load and power grid；
Thermal storage equipment constrains：
Accumulation of heat, rate of heat release are constrained to
(39) in formula, qc,tIndicate accumulation of heat rate, qd,T indicates rate of heat release, VmaxFor place capacity maximum value, VtFor accumulation of heat Thermal energy contained by the device t periods；
Regenerative capacity is constrained to
Vmin≤Vt≤Vmax (40)
Heat supply whole story state constraint is
(41) in formula, qtFor the discharge and recharge in the unit time, λtFor heat release state, λt=1 is heat release；
Gas Turbine Output constrains：
Pi,min≤Pi≤Pi,max (42)
Gas turbine Climing constant：
PMT(t)-PMT(t-1)≤Rup,MT
PMT(t-1)-PMT(t)≤Rdown,MT (43)
Gas fired-boiler units limits：
(43)-(44) in formula, QMBFor the output of gas fired-boiler, PMTIndicate the output of gas turbine, Rup,MT、Rdown,MTPoint Not Wei gas turbine Climing constant bound.
The content of step 4 is as follows：
4-1, intelligent algorithm solution procedure
Model structure is：
Wherein, fi(x) it is object function to be optimized, if object function is to minimize, takes its inverse；X is excellent to wait for The variable of change；Lb and ub is respectively the lower and upper limit constraint of variable x；Other two is the equality constraint and not of variable x respectively Equality constraint；
(1), it initializes
Iterations maxgen=1, population scale sizepop=50 are set first, it is special according to the output of each generator unit The requirement of point, the operation characteristic of energy-storage system and power grid security reliability service obtains the scheduling scheme of first time；
(2), workload demand response problem solves
Workload demand response model is：
(3), iteration updates
Particle cluster algorithm more new formula is as follows：
Wherein, ω is inertia weight；r1And r2To be distributed in the random number in [0,1] section；K is current iteration number； For individual optimum particle position；For global optimum's particle position；c1And c2For constant；V is particle rapidity；X is particle position It sets；
(4), convergence is examined
An object function is often solved to check whether to reach desired value or maximum iteration, F1、F2、F3It indicates to match respectively The regulation goal function of power grid, residents micro-capacitance sensor, thermoelectricity connection type micro-capacitance sensor, F1'、F2'、F3' indicate that power distribution network, family are used respectively The scheduling of type micro-capacitance sensor, thermoelectricity connection type micro-capacitance sensor：
Since intelligent algorithm belongs to heuritic approach, it repeatedly to calculate and average.
4-2, Benders decomposition method solution procedure
Electric system need not provide the information such as detailed micro- source parameter, scheduling scheme when solving to therrmodynamic system, simultaneously The operating status of gas fired-boiler and phase change heat accumulator in therrmodynamic system need not also share to electric system, and two systems are only The optimal output of miniature gas turbine need to be interacted and Benders cuts constraint to solve the model.Therefore, this method is realizing electricity While heat integration system optimal is dispatched, the privacy of two systems is also protected.
(1) initialization of variable
Iterations v is set to 1, definitionFor lower border value of the object function in the v times iteration, separatelyBased on electric system aleatory variable piFeasible zone, provide pvInitial value p0, i.e. pv=p0；
(2) solving model
The iterative model of electric system is：
(49) in formula, q is the result of last therrmodynamic system primal problem optimization；The object function of electric system subfunction Join the object function of micro-capacitance sensor scheduling for thermoelectricity, constraints includes electric system constraint and coupled thermomechanics constraint；Model solution P is obtained(v)Value and target function value, objective function value are the coboundary after the v times iteration
The iterative model of therrmodynamic system is：
The object function of therrmodynamic system scheduling is the thermal part in catalogue scalar functions, and constraints includes therrmodynamic system Constraint and coupled thermomechanics constraint；Model solution has obtained q(v+1)With the lower boundary of object function
(3) convergence is examined
The lower boundary for the object function that inspection therrmodynamic system primal problem is obtainedObtained by electric system subproblem Whether the coboundary tolerance of object function is less than ε, and relationship is as follows：
(51) in formula, the value of ε can with any given one be more than 0 positive number, if iteration result meets (51) formula, Obtain optimal solution：
Q=q(v)
P=p(v) (52)
If iteration result is unsatisfactory for above formula, needs to generate and cut constraint, from electric system subproblem to therrmodynamic system master Problem return Benders, which is cut, to be modified：
In formula,It is formed for bidirectional variable, also referred to as shadow price for increasing the susceptibility of object function The constraints that Benders is cut：
Compared with prior art, the present invention has the advantages that：
1, electricity distribution network model, including line loss and safe operation constraint are considered in detail；In Utilities Electric Co. and micro-capacitance sensor The solution more compromised is had found between Interest Main Body；
2, a kind of flexible customer charge demand response mode is proposed, is had to customer charge in invention more specific It divides, has fully considered the power consumption characteristics of user, met the electricity consumption idea of modern；It is negative to optimize solution user with intelligent algorithm 24 hours assignment problems of lotus, wherein theory the most advanced is then the flexible conversion of interruptible load and translatable load；
3, the power distribution network scheduling containing micro-capacitance sensor is a multidimensional, complicated, nonlinear optimal problem, using traditional Optimization algorithm difficulty in computation is larger, optimizes all kinds of object functions using particle cluster algorithm, genetic algorithm optimization demand response is negative Lotus reduces system-computed complexity, can rapidly converge to system optimal value, can be used for the optimization tune of power grid in Practical Project Degree problem.
4, thermoelectricity contact system is resolved by main and sub problem solving using Benders decomposition methods, it is complicated reduces system-computed Degree, can rapidly converge to system optimal value, can be used for the optimization problem of extensive big system in Practical Project
Description of the drawings
Fig. 1 is the solution flow chart of the method for the present invention.
Fig. 2 is the distribution net work structure figure of the method for the present invention.
Fig. 3 is the Benders decomposition method flow charts of the method for the present invention.
Specific implementation mode
1 the present invention will be further described below in conjunction with the accompanying drawings.
The step of the method for the invention, is as follows：
Step 1, the model of the power distribution network containing micro-capacitance sensor, including the scheduling of power distribution network scheduling model, residents micro-capacitance sensor are established Model, thermoelectricity connection type micro-capacitance sensor scheduling model, distribution net work structure are as shown in Fig. 2；
1-1, power distribution network independence and integrated distribution model
When power distribution network independently optimizes, the interests of microgrid are not considered, while ensureing the operation of power distribution network economic security, Make the Income Maximum of electric company.And in power distribution network and micro-capacitance sensor joint optimal operation, to optimize the scheduling of power distribution network simultaneously The regulation goal of target and two micro-capacitance sensors.When power distribution network is dispatched, its safety, reliability and economy are considered.
When doing power distribution network Optimized Operation, voltage and network loss be ensure power grid security economical operation two most criticals because Element, the present invention calculate analysis using Newton-Raphson approach；
Newton-Raphson approach Load flow calculation formula is as follows：
PG+PM-PL=Ui∑Uj(Gijcosθij+Bij sinθij) (1a)
QG+QM-QL=Ui∑Uj(Gijsinθij+Bijcosθij) (2a)
In (1a) formula, PG、PMAnd PLIndicate that major network injection is active, the active and load of two kinds of micro-capacitance sensor injection is active respectively Power；GijAnd BijConductance between respectively node i j and susceptance；UiIndicate the voltage of node i；θijBetween expression node i j Phase angle difference；QG、QL、QMIt is identical as same letter meaning in formula (1a)；
As more and more wind-powered electricity generations put into operation, the accuracy requirement of error is also higher and higher to be predicted to wind-powered electricity generation.Country Grid company provides：Short-term wind-electricity prediction error must not exceed 20%.Since wind-powered electricity generation error and its corresponding decision have in time Relevant property is herein described the uncertainty of wind-powered electricity generation error using scene method.The formation of scene method includes that scene generates Reduce two key steps with scene.
Currently, common wind-powered electricity generation error distribution mainly has normal distribution and beta distribution.The present invention is using " pre- measuring tank " The range of model split wind power output, and wind-powered electricity generation data have been subjected to standardization, section size is 0.0125.Due to three parameters Weibull distributions can express a variety of distributions by changing parameter, the present invention utilizes each section of Weibull fittings of distribution Wind-powered electricity generation error curve, with particle cluster algorithm combination maximum-likelihood method estimate parameter.
Weibull distribution probability density expression formula be
In (3a) formula, α is scale parameter；β is form parameter；γ is location parameter
Weibull distribution likelihood function be
In (4a) formula, θ=(α, beta, gamma)
Weibull distribution likelihood function logarithm be
The basic thought of scene reduction is to make the scene set before reduction and between the scene subclass finally retained Probability metrics are minimum, i.e.,
In (6a) formula, piIndicate the probability that scene i occurs,Indicate the distance between scene i and scene j, The set that J is made of the scene finally eliminated during scene reduction
The scheduling of residents micro-capacitance sensor up to dispatches mesh with the goodness of fit of realized load curve and expected load curve Mark：
L in (7a) formulat：The actual load of moment t (1~24), Lobj,tThe target load of moment t (1~24), Lfirm,t、 Ltran,t、Lint,tAnd Ladj,tFirm demand, transferable load, interruptible load and adjustable load are indicated respectively.K (t) is t Moment electricity price.
Accumulator has had been provided with scientific and reasonable mathematical model as one of most common energy storage device.Accumulator Life consumption is inseparable with its state-of-charge, charge and discharge number, charge-discharge electric power:
Cch-dis=CG+Cch+Cdis (8a)
In (8a)-(10a) formula, Cch-disIndicate storage hair cost, CGIndicate unit quantity of electricity purchase electricity price, Cch, CdisTo store The life consumption cost of battery charging and discharging, fuel cell mode are to store CinitBattery buys cost of investment；NSBFor cycle charge-discharge Number.It can be seen that charge and discharge cost (Cch、Cdis) by charge and discharge cost coefficient (lch、ldis) determine, and charge and discharge cost coefficient By charge and discharge whole story state-of-charge (SOCstart, SOCend), charge-discharge electric power (Pch, Pdis) and charge and discharge impact factor (kch, kdis) determined.
1-3, thermoelectricity connection type micro-capacitance sensor mathematical model
In the present invention, thermoelectricity connection type micro-capacitance sensor includes miniature gas turbine, gas fired-boiler, wind turbine, photovoltaic, energy-storage system With thermoelectricity load, the related units limits of meter and electric energy exchange benefit and each micro battery between micro-capacitance sensor and power distribution network are established Using electric heating Income Maximum as the micro-capacitance sensor economic load dispatching model of regulation goal.
The cost model of photovoltaic generation unit is：
F(PPV,t)=aIPPPV,t+GEPPV,t (11a)
In (11a) formula：
In (11a)-(12a) formula, a indicates the cost coefficient of photovoltaic installation；GEIndicate photovoltaic operation expense coefficient； IPFor the specific investment cost cost of photovoltaic generation unit；N indicates service life, is 20 years.
The cost model of wind turbine power generation unit is：
F(PW,t)=aIPPW,t+GEPW,t (13a)
In (13a) formula, the same photovoltaic generation unit of meaning of identical variable；
Phase change heat accumulator mathematical model：
Phase-transition heat-storage, based on phase-changing energy storage material, using latent heat of phase change store heat, mainly include sensible heat heat accumulation, Heat chemistry heat accumulation and phase-change thermal storage.Compared to the above two, the storage density of phase-change thermal storage is big and temperature is constant, in solar heat Using, the existing application of recycling of waste heat and waste heat, have broad application prospects in power scheduling industry.
Due to storage heater can not spontaneous generation and consumption energy, only be used as energy terminal, therefore, consider phase transformation When the cost of storage heater, its operation expense and depreciable cost are only considered：
Cm,TB=Cmm,TBHTB (15a)
L is the annual interest fixed rate of interest in (14a), (15a) formula；Cint,TBIndicate depreciation of the phase change heat accumulator within investment cycle at This；CTBFor the cost of investment of first stage of construction；Cm,TBIndicate operation expense；Cmm,TBFor unit power maintenance cost；HTBFor Thermal power.
Gas turbine mathematical model：
Gas turbine can carry out power generation and heat supply using pluralities of fuels such as natural gas, biogas.The cogeneration of gas turbine The economic mathematical model calculation formula of system is as follows：
Qhe=QMT×ηrec×COPhe (18a)
In (16a)-(19a) formula, CMTFor one day fuel cost of miniature combustion engine；CngFor Gas Prices, 2 yuan/m3； LHVng For natural gas low heat value, 9.7kwh/m3；PMT,iFor the net electromotive power output of moment i, kW；ηMT,iFor the unit efficiency of moment i. In formula, QMTFor combustion turbine exhaustion excess heat, kW；ηlFor gas turbine radiation loss coefficient, 0.03 is taken herein；QheFor combustion gas The heating capacity that turbine fume afterheat provides, kW；COPheFor heating efficiency；ηrecFor flue gas waste heat recovery efficiency；T0For environment temperature Degree, K；T1、T2The respectively temperature of waste heat flue gas disengaging shore cold, K, T1、T2Take 573.15K and 423.15K.
Gas fired-boiler mathematical model：
For gas fired-boiler as main heat-producing device in cogeneration system, the consumption of natural gas and its calorific value are close Correlation ignores the influence of other factors to simplify the analysis, obtains the hot relationship of gas in gas fired-boiler operational process:
In (20a) formula, ηBFor the actual operating efficiency of gas fired-boiler, 0.85 is taken；FBFor gas fired-boiler gas consumption, Nm3；Q is natural gas low heat valve, 9.7kW/m3。
The burning of natural gas will produce polluted gas and solid particle, and to ensure environmental benefit, the improvement of emission is received In the operating cost for entering micro-capacitance sensor.The gas control emission cost function of miniature combustion engine is as follows：
In (21a) formula, αk、λkThe respectively external discount cost of discharge type k, emission factor, M are discharge type (NOX、SO2Or CO2)。
Step 2, micro-capacitance sensor and the independent object function with combined dispatching of power distribution network are established；
Power distribution network using Utilities Electric Co.'s Income Maximum as object function, with demand charge paid minimum by residents micro-capacitance sensor As object function, thermoelectricity connection type micro-capacitance sensor establishes power distribution network and micro-capacitance sensor Joint economics using maximum of getting a profit as object function Scheduling model is：
In (22a) formula：PAL,tFor the total load in scene s；CtFor grid company sale of electricity electricity price；CF1、CF2To exchange power Price；PF1,tAnd PF2,tRespectively electric company t periods and micro-capacitance sensor exchanges power；PTR,tIt it is the electric company t periods to defeated The power of power grid purchase；CPCPAnd CPCWRespectively abandon the punishment electricity price that wind abandons light；ΔPPV,tWith Δ PW,tIt respectively abandons wind and abandons light Power.
Residents micro-capacitance sensor is minimum as target using family's same day total electricity bill expenditure：
C indicates that family microgrid day total electricity bill, total electricity bill are that negative value indicates profit in (23a) formula；Cb,tFor power purchase price row Vector, Pb,tIt is user from the purchase of electricity of power grid, Cch-disFor the operation and maintenance cost of energy-storage system.KiPi,tIndicate distributed electrical The maintenance cost in source, it is directly proportional to the output of distributed generation resource.
Thermoelectricity connection type micro-capacitance sensor is maximum as regulation goal to get a profit：
In (24a) formula, CP、CHThe price of thermal energy and electric energy in micro-capacitance sensor is indicated respectively；PLoad、QLoadRespectively indicate heat, Electric load；F(PMT)、F(QMB)、F(PEB)、F(QTB)、F(PPV) and F (PW) indicate that gas turbine, gas fired-boiler, electric power storage are set respectively The operation expense of standby, thermal storage equipment, photovoltaic and wind turbine；F(PGrid) it is purchases strategies of the micro-capacitance sensor from power distribution network；
Step 3, the independent constraints with combined dispatching of the power distribution network containing micro-capacitance sensor is established；
3-1, power distribution network constraints：
(1) power-balance
PTR,t+PF1,t+PF2,t=PAL,t (25a)
In (25a) formula, PTR,tIt is Utilities Electric Co. from higher level's power grid power purchase power；PF1,tIt is electric company from the micro- electricity of residents Net purchase electrical power；PF2,tIt is Utilities Electric Co. from enterprise micro-capacitance sensor power purchase power；PAL,tBy power distribution network band total load；
(2) power distribution network interacts power limit with micro-capacitance sensor
In (26a) formula,The bound that respectively power distribution network is exchanged with microgrid power；
(3) grid branch trend constraint
In (27a) formula,The respectively strength of current of circuit m, minimum trend,For the t moment The trend of circuit m；
(4) when operation of power networks, voltage and current must be in rated range, and network loss is no more than maximum value：
Footmark max indicates that its maximum value, footmark min indicate its minimum value in (28a)-(29a) formula；U and I are indicated respectively The voltage and current of circuit；
3-2, residents micro-capacitance sensor constraints：
(1) electric equilibrium of residents micro-capacitance sensor is constrained to：
Pgrid1(t)+Ppv1(t)+Pbs1(t)=Lload1(t) (30a)
In (30a) formula, Pgrid1(t) indicate residents micro-capacitance sensor and power distribution network exchanges power；Ppv1(t) it is family's light to be Lie prostrate generator unit generated energy hourly；Pbs1(t) it is the charge or discharge power of accumulator；Lload1(t) it is to be rung by demand After should adjusting, the load at t (1~24) moment；
When demand response, the constraints that Load Regulation need to meet is：
Pflexmin,i≤Pflex,i≤Pflexmax,i (31a)
In (31a) formula, Pflex,iIt indicates that load is adjusted, bound is constrained to its power adjustable range；
The constraints that load rejection need to meet is：
Nint≤Nintmax (32a)
In (32a) formula, NintIndicate interruptible load number, NintmaxFor maximum interruptible load number；
(3) energy storage charge and discharge are constrained to：
SOCmin(t)≤SOC(t)≤SOCmax(t) (33a)
Xt∈ { 0,1 }, Yt∈ { 0,1 }, Xt·Yt=0 (34a)
SOC (0)=SOC (T) (35a)
For accumulator, to prevent accumulator super-charge and crossing the generation put, the state-of-charge (SOC) of accumulator should meet Upper limit value and lower limit value constrains；Accumulator is not simultaneously in charging and discharging state, therefore the charging and discharging state X of accumulatort、YtIt needs Meet 0-1 constraints；Accumulator removes residents micro-capacitance sensor, remaining power grid energy shape during the running optimizatin of the system of participation State (SOC) need to meet in the equal constraint of whole story dispatching cycle；Simultaneously, it is contemplated that accumulator cell charging and discharging watt level and battery Service life it is related, charge and discharge maximum power P in the unit intervalch-disIt need to be in limitation range；
3-3, the constraint of thermoelectricity connection type micro-capacitance sensor：
Electric equilibrium constrains：
Pgrid(t)+Ppv(t)+Pw(t)+PMT(t)+PEB(t)=PLoad(t) (37a)
Thermal balance constrains：
QMT+QTB+QMB=QLoad (38a)
In (37a)-(38a) formula, P indicates that electric energy, Q indicate thermal energy, MT, MB, EB, TB, PV, W, Load, grid points of subscript It Biao Shi not gas turbine, gas fired-boiler, electric energy storage device, thermal storage equipment, photovoltaic, wind turbine, load and power grid；
Thermal storage equipment constrains：
Accumulation of heat, rate of heat release are constrained to
In (39a) formula, qc,tIndicate accumulation of heat rate, qd, t expression rate of heat releases, VmaxFor place capacity maximum value, VtTo store Thermal energy contained by the thermal t periods；
Regenerative capacity is constrained to
Vmin≤Vt≤Vmax (40a)
Heat supply whole story state constraint is
In (41a) formula, qtFor the discharge and recharge in the unit time, λtFor heat release state, λt=1 is heat release；
The units limits of wind turbine, photovoltaic and gas turbine：
Pi,min≤Pi≤Pi,max (42a)
Gas turbine Climing constant：
PMT(t)-PMT(t-1)≤Rup,MT
PMT(t-1)-PMT(t)≤Rdown,MT (43a)
Gas fired-boiler units limits：
In (43a)-(44a) formula, QMBFor the output of gas fired-boiler, PMTIndicate the output of gas turbine, Rup,MT、Rdown,MTPoint Not Wei gas turbine Climing constant bound.
Step 4, intelligent algorithm is the mutual exchanges and cooperation between bion and individual in natural imitation circle, with letter Single, limited individual behavior and intelligence form entire group whole capability difficult to the appraisal by interaction；Population is calculated Method and genetic algorithm algorithm are simple, parameter is less, the adjustment of parameter and setting are also more convenient, and genetic algorithm is being handled It is more mature in terms of dispersed problem, therefore the present invention chooses the scheduling of PSO Algorithm power distribution network, selects genetic algorithm excellent Change load；Thermoelectricity is solved using Benders decomposition methods and joins micro-capacitance sensor Optimal Scheduling.According to the variable class of thermoelectricity contact system Type is broken down into heating power primal problem and the electric power subproblem of micro-capacitance sensor, solution flow such as Fig. 3 institutes of Benders decomposition methods Show.
4-1, intelligent algorithm solution procedure；Model structure is：
Wherein, fi(x) it is object function to be optimized, if object function is to minimize, takes its inverse；X is excellent to wait for The variable of change；Lb and ub is respectively the lower and upper limit constraint of variable x；Other two is the equality constraint and not of variable x respectively Equality constraint；
(1), it initializes
Iterations maxgen=1, population scale sizepop=50 are set first, it is special according to the output of each generator unit The requirement of point, the operation characteristic of energy-storage system and power grid security reliability service obtains the scheduling scheme of first time；
(2), workload demand response problem solves
Workload demand response model is：
Workload demand response refers to making the load of each period close to the desired load of user by demand response means Value；This is the optimization problem of discrete variable；By the selection of genetic algorithm, intersection, variation sequence of operations, keep P values closest M values；
(3), iteration updates
In the present invention, genetic algorithm optimization is only used when workload demand responds, and when other conditions solve, uses grain Swarm optimization.
Particle cluster algorithm more new formula is as follows：
Xk+1=Xk+Vk+1 (47a)
Wherein, ω is inertia weight；r1And r2To be distributed in the random number in [0,1] section；K is current iteration number； For individual optimum particle position；For global optimum's particle position；c1And c2For constant；V is particle rapidity；X is particle position It sets；
(4), convergence is examined
An object function is often solved to check whether to reach desired value or maximum iteration, F1、F2、F3It indicates to match respectively The regulation goal function of power grid, residents micro-capacitance sensor, thermoelectricity connection type micro-capacitance sensor, F1'、F2'、F3' indicate that power distribution network, family are used respectively The scheduling of type micro-capacitance sensor, thermoelectricity connection type micro-capacitance sensor：
Since intelligent algorithm belongs to heuritic approach, it repeatedly to calculate and average.
4-2, Benders decomposition method solution procedure
Electric system need not provide the information such as detailed micro- source parameter, scheduling scheme when solving to therrmodynamic system, simultaneously The operating status of gas fired-boiler and phase change heat accumulator in therrmodynamic system need not also share to electric system, and two systems are only The optimal output of miniature gas turbine need to be interacted and Benders cuts constraint to solve the model.Therefore, this method is realizing electricity While heat integration system optimal is dispatched, the privacy of two systems is also protected.
(1) initialization of variable
First, iterations v is set to 1, definitionFor lower border value of the object function in the v times iteration, separatelyBased on electric system aleatory variable piFeasible zone, provide pvInitial value p0, i.e. pv=p0。
(2) solving model
The iterative model of electric system is：
In (49a) formula, q is the result of last therrmodynamic system primal problem optimization；The object function of electric system subfunction Join the object function of micro-capacitance sensor scheduling for thermoelectricity, constraints includes electric system constraint and coupled thermomechanics constraint.Model solution P is obtained(v)Value and target function value, objective function value are the coboundary after the v times iteration
The iterative model of therrmodynamic system is：
The object function of therrmodynamic system scheduling is the thermal part in catalogue scalar functions, and constraints includes therrmodynamic system Constraint and coupled thermomechanics constraint.Model solution has obtained q(v+1)With the lower boundary of object function
(3) convergence is examined
The lower boundary for the object function that inspection therrmodynamic system primal problem is obtainedObtained by electric system subproblem Whether the coboundary tolerance of object function is less than ε, and relationship is as follows：
In (51a) formula, the value of ε, if iteration result meets above formula, can be obtained with any given one positive number more than 0 Go out optimal solution：
Q=q(v)
P=p(v) (52a)
If iteration result is unsatisfactory for above formula, needs to generate and cut constraint, from electric system subproblem to therrmodynamic system master Problem return Benders, which is cut, to be modified：
In formula,It is formed for bidirectional variable, also referred to as shadow price for increasing the susceptibility of object function The constraints that Benders is cut：

Claims (1)

1. a kind of independent method for solving with combined dispatching of power distribution network containing micro-capacitance sensor, which is characterized in that the method includes with Lower step：
Step 1, establish the electricity distribution network model containing micro-capacitance sensor, including power distribution network scheduling model, residents micro-capacitance sensor scheduling model, Thermoelectricity connection type micro-capacitance sensor scheduling model；
Step 2, micro-capacitance sensor and the independent object function with combined dispatching of power distribution network are established；
Step 3, the independent constraints with combined dispatching of the power distribution network containing micro-capacitance sensor is established；
Step 4, the scheduling of PSO Algorithm power distribution network is chosen, genetic algorithm optimization load is selected；It is decomposed using Benders Method solves thermoelectricity and joins micro-capacitance sensor Optimal Scheduling.
The content of step 1 is as follows：
1-1, power distribution network scheduling model
When power distribution network is dispatched, consideration is exchanged from the purchase of electricity of major network, with the electric energy of two class micro-capacitance sensors and scene consumption situation；
Newton-Raphson approach Load flow calculation formula is as follows：
(1)-(2) in formula, PG、PMAnd PLIndicate that major network injects the active and load of active, two kinds of micro-capacitance sensor injection and has respectively Work(power；GijAnd BijConductance between respectively node i j and susceptance；UiIndicate the voltage of node i；θijBetween expression node i j Phase angle difference；QG、QL、QMIndicate that major network injects idle, two kinds of micro-capacitance sensor and injects idle and reactive load power respectively；
The uncertainty of wind-powered electricity generation error is described using scene method, the formation of scene method includes that scene generates and scene reduction Two key steps；
Using the range of the model split wind power output of " pre- measuring tank ", wind-powered electricity generation data standardization is subjected to, section size is 0.0125.Using the wind-powered electricity generation error curve in each section of Weibull fittings of distribution, with particle cluster algorithm combination maximum-likelihood method Estimate parameter；
Weibull distribution probability density expression formula be
(3) in formula, α is scale parameter；β is form parameter；γ is location parameter；
Weibull distribution likelihood function be
(4) in formula, θ=(α, beta, gamma)；
Weibull distribution likelihood function logarithm be
Scene reduction is to keep the scene set before reduction and the probability metrics between the scene subclass finally retained minimum, i.e.,
(6) in formula, piIndicate the probability that scene i occurs,Indicate the distance between scene i and scene j, J is field The set that the scene finally eliminated during scape reduction is formed；
1-2, residents micro-capacitance sensor models
Load is divided into four classes：Firm demand, interruptible load, adjustable load (power adjustable) and translatable load are (when use Section is adjustable)；
Workload demand response model be：
(7) L in formulatFor the actual load of (1~24) moment t, Lobj,tFor the target load of (1~24) moment t, Lfirm,t、 Ltran,t、Lint,tAnd Ladj,tFirm demand, transferable load, interruptible load and adjustable load are indicated respectively；When K (t) is t Carve electricity price.
The operating cost of accumulator includes its charging cost and life consumption cost；
Cch-dis=CG+Cch+Cdis (8)
(8)-(10) in formula, Cch-disIndicate storage hair cost, CGIndicate unit quantity of electricity purchase electricity price, Cch, CdisFor accumulator charge and discharge The life consumption cost of electricity, fuel cell mode are to store CinitBattery buys cost of investment；NSBFor cycle charge-discharge number；It can be seen that Charge and discharge cost (Cch、Cdis) by charge and discharge cost coefficient (lch、ldis) determine, and charge and discharge cost coefficient is begun by charge and discharge Last state-of-charge (SOCstart, SOCend), charge-discharge electric power (Pch, Pdis) and charge and discharge impact factor (kch, kdis) determined.
1-3, thermoelectricity connection type micro-capacitance sensor mathematical model
Thermoelectricity connection type micro-capacitance sensor include miniature gas turbine, gas fired-boiler, wind turbine, photovoltaic, energy-storage system and thermoelectricity load, meter and Electric energy between micro-capacitance sensor and power distribution network exchanges the related units limits of benefit and each micro battery, establishes with electric heating Income Maximum For the micro-capacitance sensor economic load dispatching model of regulation goal；
The cost model of photovoltaic generation unit is：
F(PPV,t)=aIPPPV,t+GEPPV,t (11)
(11) in formula：
(11)-(12) in formula, a indicates the cost coefficient of photovoltaic installation；GEIndicate photovoltaic operation expense coefficient；IPFor photovoltaic The specific investment cost cost of generator unit；N indicates service life, is 20 years；
The cost model of wind turbine power generation unit is：
F(PW,t)=aIPPW,t+GEPW,t (13)
(13) in formula, the same photovoltaic generation unit of meaning of identical variable；
Phase change heat accumulator mathematical model：
When considering the cost of phase change heat accumulator, its operation expense and depreciable cost are only considered：
Cm,TB=Cmm,TBHTB (15)
(14), l is the annual interest fixed rate of interest in (15) formula；Cint,TBIndicate depreciable cost of the phase change heat accumulator within investment cycle；CTBTo build If the cost of investment at initial stage；Cm,TBIndicate operation expense；Cmm,TBFor unit power maintenance cost；HTBFor thermal power；
Gas turbine mathematical model：
The economic mathematical model calculation formula of the cogeneration system of gas turbine is as follows：
Qhe=QMT×ηrec×COPhe (18)
(16)-(19) in formula, CMTFor one day fuel cost of miniature combustion engine；CngFor Gas Prices, 2 yuan/m3；LHVngFor natural gas Low heat value, 9.7kwh/m3；PMT,iFor the net electromotive power output of moment i, kW；ηMT,iFor the unit efficiency of moment i；In formula, QMTFor Combustion turbine exhaustion excess heat, kW；ηlFor gas turbine radiation loss system；QheThe heating provided for gas turbine flue gas waste heat Amount, kW；COPheFor heating efficiency；ηrecFor flue gas waste heat recovery efficiency；T0For environment temperature, K；T1、T2Respectively waste heat flue gas Pass in and out the temperature of shore cold, K, T1、T2Take 573.15K and 423.15K；
Gas fired-boiler mathematical model：
The hot relationship of gas in gas fired-boiler operational process:
(20) in formula, ηBFor the actual operating efficiency of gas fired-boiler, 0.85 is taken；FBFor gas fired-boiler gas consumption, Nm3；q For natural gas low heat valve, 9.7kW/m3；
The gas control emission cost function of miniature combustion engine is as follows：
(21) in formula, αk、λkThe respectively external discount cost of discharge type k, emission factor, M are discharge type.
The content of step 2 is as follows：
For power distribution network using Utilities Electric Co.'s Income Maximum as object function, residents micro-capacitance sensor is minimum as mesh using demand charge expenditure Scalar functions, thermoelectricity connection type micro-capacitance sensor is maximum as object function to get a profit, and establishes power distribution network and dispatches mould with micro-capacitance sensor Joint economics Type is：
(22) in formula：PAL,tFor the total load in scene s；CtFor grid company sale of electricity electricity price；CF1、CF2To exchange the valence of power Lattice；PF1,tAnd PF2,tRespectively electric company t periods and micro-capacitance sensor exchanges power；PTR,tIt it is the electric company t periods to power transmission network The power of purchase；CPCPAnd CPCWRespectively abandon the punishment electricity price that wind abandons light；ΔPPV,tWith Δ PW,tRespectively abandon the work(that wind abandons light Rate；
Residents micro-capacitance sensor is minimum as target using family's same day total electricity bill expenditure：
(23) C indicates that family microgrid day total electricity bill, total electricity bill are that negative value indicates profit in formula；Cb,tFor power purchase price row vector, Pb,tIt is user from the purchase of electricity of power grid, Cch-disFor the operation and maintenance cost of energy-storage system；KiPi,tIndicate the dimension of distributed generation resource Cost is protected, it is directly proportional to the output of distributed generation resource；
Thermoelectricity connection type micro-capacitance sensor is maximum as regulation goal to get a profit：
(24) in formula, CP、CHThe price of thermal energy and electric energy in micro-capacitance sensor is indicated respectively；PLoad、QLoadOptimized distributionl is indicated respectively；F (PMT)、F(QMB)、F(PEB)、F(QTB)、F(PPV) and F (PW) indicate that gas turbine, gas fired-boiler, electric energy storage device, accumulation of heat are set respectively The operation expense of standby, photovoltaic and wind turbine；F(PGrid) it is purchases strategies of the micro-capacitance sensor from power distribution network.
The content of step 3 is as follows：
3-1, power distribution network constraints：
(1) power-balance
PTR,t+PF1,t+PF2,t=PAL,t (25)
(25) in formula, PTR,tIt is Utilities Electric Co. from higher level's power grid power purchase power；PF1,tIt is electric company from residents micro-capacitance sensor power purchase Power；PF2,tIt is Utilities Electric Co. from thermoelectricity connection type micro-capacitance sensor power purchase power；PAL,tBy power distribution network band total load；
(2) power distribution network interacts power limit with micro-capacitance sensor
(26) in formula,The bound that respectively power distribution network is exchanged with microgrid power；
(3) grid branch trend constraint
(27) in formula,The respectively strength of current of circuit m, minimum trend,For t moment circuit m's Trend；
(4) when operation of power networks, voltage and current must be in rated range, and network loss is no more than maximum value：
(28)-(29) footmark max indicates that its maximum value, footmark min indicate its minimum value in formula；U, I indicates the electricity of circuit respectively Pressure and electric current；
3-2, residents micro-capacitance sensor constraints：
(1) electric equilibrium of residents micro-capacitance sensor is constrained to：
Pgrid1(t)+Ppv1(t)+Pbs1(t)=Lload1(t) (30)
(30) in formula, Pgrid1(t) indicate residents micro-capacitance sensor and power distribution network exchanges power；Ppv1(t) it is family's photovoltaic generation to be Unit generated energy hourly；Pbs1(t) it is the charge or discharge power of accumulator；Lload1(t) it is to be adjusted by demand response Afterwards, the load at t (1~24) moment；
When demand response, the constraints that Load Regulation need to meet is：
Pflexmin,i≤Pflex,i≤Pflexmax,i (31)
(31) in formula, Pflex,iIt indicates that load is adjusted, bound is constrained to its power adjustable range；
The constraints that load rejection need to meet is：
Nint≤Nintmax (32)
(32) in formula, NintIndicate interruptible load number, NintmaxFor maximum interruptible load number；
(2) energy storage charge and discharge are constrained to：
SOCmin(t)≤SOC(t)≤SOCmax(t) (33)
Xt∈ { 0,1 }, Yt∈ { 0,1 }, Xt·Yt=0 (34)
SOC (0)=SOC (T) (35)
(33)-(36) in formula, SOC indicates the state-of-charge of battery, Pch-disIndicate the charge-discharge velocity of battery；The energy storage constrains Model is equally applicable to thermoelectricity connection type micro-capacitance sensor；
3-3, the constraint of thermoelectricity connection type micro-capacitance sensor：
Electric equilibrium constrains：
Pgrid(t)+Ppv(t)+Pw(t)+PMT(t)+PEB(t)=PLoad(t) (37)
Thermal balance constrains：
QMT+QTB+QMB=QLoad (38)
(37)-(38) in formula, P indicates that electric energy, Q indicate thermal energy, and subscript MT, MB, EB, TB, PV, W, Load, grid are indicated respectively Gas turbine, gas fired-boiler, electric energy storage device, thermal storage equipment, photovoltaic, wind turbine, load and power grid；
Thermal storage equipment constrains：
Accumulation of heat, rate of heat release are constrained to
(39) in formula, qc,tIndicate accumulation of heat rate, qd,T indicates rate of heat release, VmaxFor place capacity maximum value, VtFor regenerative apparatus Thermal energy contained by the t periods；
Regenerative capacity is constrained to
Vmin≤Vt≤Vmax (40)
Heat supply whole story state constraint is
(41) in formula, qtFor the discharge and recharge in the unit time, λtFor heat release state, λt=1 is heat release；
Gas Turbine Output constrains：
Pi,min≤Pi≤Pi,max (42)
Gas turbine Climing constant：
PMT(t)-PMT(t-1)≤Rup,MT
PMT(t-1)-PMT(t)≤Rdown,MT (43)
Gas fired-boiler units limits：
(43)-(44) in formula, QMBFor the output of gas fired-boiler, PMTIndicate the output of gas turbine, Rup,MT、Rdown,MTRespectively fire The bound of gas-turbine Climing constant.
The content of step 4 is as follows：
4-1, intelligent algorithm solution procedure
Model structure is：
Wherein, fi(x) it is object function to be optimized, if object function is to minimize, takes its inverse；X is change to be optimized Amount；Lb and ub is respectively the lower and upper limit constraint of variable x；Other two be respectively variable x equality constraint and inequality about Beam；
(1), it initializes
Iterations maxgen=1, population scale sizepop=50 are set first, according to the output feature of each generator unit, storage The requirement of the operation characteristic and power grid security reliability service of energy system, obtains the scheduling scheme of first time；
(2), workload demand response problem solves
Workload demand response model is：
(3), iteration updates
Particle cluster algorithm more new formula is as follows：
Wherein, ω is inertia weight；r1And r2To be distributed in the random number in [0,1] section；K is current iteration number；For individual Optimum particle position；For global optimum's particle position；c1And c2For constant；V is particle rapidity；X is particle position；
(4), convergence is examined
An object function is often solved to check whether to reach desired value or maximum iteration, F1、F2、F3Respectively indicate power distribution network, The regulation goal function of residents micro-capacitance sensor, thermoelectricity connection type micro-capacitance sensor, F '1、F′2、F′3Power distribution network, the micro- electricity of residents are indicated respectively The scheduling of net, thermoelectricity connection type micro-capacitance sensor：
Since intelligent algorithm belongs to heuritic approach, it repeatedly to calculate and average.
4-2, Benders decomposition method solution procedure
Electric system need not provide the information such as detailed micro- source parameter, scheduling scheme, while heating power to therrmodynamic system when solving The operating status of gas fired-boiler and phase change heat accumulator in system need not also share to electric system, and two systems only need to interact The optimal output and Benders of miniature gas turbine cut constraint to solve the model.Therefore, this method is realizing electric heating joint system While system optimal scheduling, the privacy of two systems is also protected.
(1) initialization of variable
Iterations v is set to 1, definitionFor lower border value of the object function in the v times iteration, separatelyIt is based on Electric system aleatory variable piFeasible zone, provide pvInitial value p0, i.e. pv=p0；
(2) solving model
The iterative model of electric system is：
(49) in formula, q is the result of last therrmodynamic system primal problem optimization；The object function of electric system subfunction is thermoelectricity Join the object function of micro-capacitance sensor scheduling, constraints includes electric system constraint and coupled thermomechanics constraint；Model solution has obtained p(v)Value and target function value, objective function value are the coboundary after the v times iteration
The iterative model of therrmodynamic system is：
Therrmodynamic system scheduling object function be catalogue scalar functions in thermal part, constraints include therrmodynamic system constraint and Coupled thermomechanics constrain；Model solution has obtained q(v+1)With the lower boundary of object function
(3) convergence is examined
The lower boundary for the object function that inspection therrmodynamic system primal problem is obtainedWith the target letter obtained by electric system subproblem Whether several coboundary tolerances is less than ε, and relationship is as follows：
(51) in formula, the value of ε can be with any given one positive number more than 0, if iteration result meets (51) formula, it is concluded that most Excellent solution：
Q=q(v)
P=p(v) (52)
If iteration result is unsatisfactory for above formula, needs to generate and cut constraint, from electric system subproblem to therrmodynamic system primal problem Return Benders, which is cut, to be modified：
In formula,Form what Benders was cut for bidirectional variable, also referred to as shadow price for increasing the susceptibility of object function Constraints：
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