CN107769266A - A kind of Multiple Time Scales generate electricity and standby combined optimization method - Google Patents

A kind of Multiple Time Scales generate electricity and standby combined optimization method Download PDF

Info

Publication number
CN107769266A
CN107769266A CN201710846018.7A CN201710846018A CN107769266A CN 107769266 A CN107769266 A CN 107769266A CN 201710846018 A CN201710846018 A CN 201710846018A CN 107769266 A CN107769266 A CN 107769266A
Authority
CN
China
Prior art keywords
mrow
msub
msubsup
power generating
generating unit
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.)
Pending
Application number
CN201710846018.7A
Other languages
Chinese (zh)
Inventor
陆春良
赵巍
吴华华
张俊
由新红
叶承晋
杨晓雷
丁
丁一
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University ZJU
State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Zhejiang University ZJU
State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhejiang University ZJU, State Grid Corp of China SGCC, State Grid Zhejiang Electric Power Co Ltd filed Critical Zhejiang University ZJU
Priority to CN201710846018.7A priority Critical patent/CN107769266A/en
Publication of CN107769266A publication Critical patent/CN107769266A/en
Pending legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Battery Electrode And Active Subsutance (AREA)

Abstract

The present invention proposes that a kind of Multiple Time Scales generate electricity and standby combined optimization method, this method is with electric power system operation standby by up-regulation load is standby, downward load is standby and spinning reserve forms, from a few days ago, in the daytime, real-time three time scales refine step by step, progressive fine analog power system generation schedule.Multiple Time Scales proposed by the present invention generate electricity can be with accurate simulation large-scale power system planned project with standby integrated distribution model; the system operation that each every generating set startup-shutdown state of period and active power output is calculated and undertakes is standby; modern power systems fired power generating unit random fault, uncertain load fluctuation and extensive the regenerative resource running environment such as output at random are suitable for, it is significant to power system security economical operation.

Description

A kind of Multiple Time Scales generate electricity and standby combined optimization method
Technical field
The present invention relates to Operation of Electric Systems and planning field, and in particular to a kind of Multiple Time Scales generate electricity combines with standby Optimization method.
Background technology
The access of extensive regenerative resource is filled with bigger uncertainty for safe operation of power system, using wind-powered electricity generation as Example, the uncertainty of large-scale wind power output prediction will cause to adjust output water repeatedly during power system conventional power unit Real-Time Scheduling It is flat, increase fired power generating unit abrasion and the waste of resource has been aggravated during the system equilibrium of supply and demand is maintained, in addition generating set Regulation repeatedly add power grid security risk, to avoid the generation of power outage, operating agency needs to retain bigger standby Capacity.Solved accordingly, it is considered to which the power system of large-scale wind power access generates electricity with operation standby configuration problems demand.At present, root According to China《Power system security fire protection technology》, spare capacity size is the 10% of peak load, and maximum one more than system Generating set capacity.Large-scale wind power access require system need to retain greater proportion it is standby with tackle wind power output uncertainty, Thereby result in cost of electricity-generating greatly to waste, therefore, and wind power output predicted value is larger in error a few days ago, at the fire formulated a few days ago Group of motors generate electricity with alternative plan in real time execution with very big fluctuation.
The content of the invention
In view of the shortcomings of the prior art, the present invention proposes that a kind of Multiple Time Scales generate electricity and standby combined optimization method, pin The characteristics of reducing with predicted time yardstick to new energy and load prediction error and constantly reducing, refines, progressive essence step by step Thin simulation power system generation schedule, improve the reliability and economy of Operation of Electric Systems.Concrete technical scheme is as follows：
A kind of Multiple Time Scales generate electricity and standby combined optimization method, as shown in figure 1, this method comprises the following steps：
Step 1：Build Optimized model, in the daytime Optimized model, real-time optimization model a few days ago；
Described Optimized model a few days ago be with power system generate electricity and the minimum object function of standby totle drilling cost, using hour as The MILP model of unit of account；
The object function of Optimized model is as follows a few days ago：
In formula, the fired power generating unit in i expression systems；
T represents the period of Optimized model a few days ago, t=1,2 ... ..., T；
FGi,t(PGi(t)) it is thermal power unit operation cost,
FGi,t(PGi(t))=ai·(PGi(t))2+bi·(PGi(t))+ci (2)
PGi(t) active power output values of the fired power generating unit i in period t is represented；
ai、bi、ciRepresent and fired power generating unit coa consumption rate coefficient correlation；
SUiFor fired power generating unit start shooting cost,
SUi=Si·αi(t)·(1-αi(t-1)) (3)
αi(t) startup-shutdown states of the unit i in the t periods is represented, if unit is started shooting, αi(t)=1, if compressor emergency shutdown, αi(t)=0；αi(t-1) startup-shutdown states of the unit i in the t-1 periods is represented, if unit is started shooting, αi(t-1)=1, if unit Shut down, then αi(t-1)=0；
SiRepresent fired power generating unit start cost coefficient；
Fi emergency(t) emergency duty cost is provided for fired power generating unit,
Represent emergency reserve capacities of the fired power generating unit i in period t；
Represent fired power generating unit i unit emergency duty cost coefficient；
Fi up(t) up-regulation load stand-by cost is provided for fired power generating unit,
Represent up-regulation reserve capacity for load variation in power of the fired power generating unit i in period t；
Represent fired power generating unit i unit up-regulation load stand-by cost coefficient；
Fi down(t) provided for fired power generating unit and lower load stand-by cost,
Represent downward reserve capacity for load variation in power of the fired power generating unit i in period t；
Represent that fired power generating unit i unit lowers load stand-by cost coefficient；
Optimized model constraints is as follows a few days ago：
(1) power flow equation
In formula, r represents load bus, r=r1,r2,...,R；
Generation of electricity by new energy unit in w expression systems；
PGw(t) maximum active power output predicted values of the generation of electricity by new energy unit w in period t is represented；
ΔPGw(t) represent that generation of electricity by new energy unit w active power output dispatch values in period t are predicted with its maximum active power output Value difference value；
Dr(t) a few days ago prediction load value of the r nodes in period t is represented；
B represents system interconnection parameter；
θ represents node power angle；
(2) section tidal current constrains
-Lmax≤Ptrans(t)≤LmaxT=1,2 ..., T (8)
In formula, Ptrans(t) the power flow momentum on interconnection is represented；
LmaxRepresent branch power transmission maximum；
(3) generator output constrains
In formula,Represent generating set i maximum generation capacity；
In formula,PGi Represent fired power generating unit i minimum generating capacity；
(4) spare capacity constrains
In formula,REup Expression system raises the standby minimum essential requirement of load；
REdown Expression system lowers the standby minimum essential requirement of load；
REemergency Represent the standby minimum essential requirement of systematic failures；
(5) the standby climbing rate constraint of fired power generating unit
In formula, Rampratei upExpression system raises the standby climbing demand of load；
Rampratei downExpression system lowers the standby climbing demand of load；
Rampratei emergencyRepresent the standby climbing demand of systematic failures；
(6) fired power generating unit minimum startup-shutdown constrains
In formula：DiFired power generating unit minimum downtime is represented, unit is hour；
OiThe fired power generating unit minimum available machine time is represented, unit is hour；
(7) fired power generating unit climbing rate constrains
In formula,Represent fired power generating unit up-regulation climbing rate limitation；
Represent that fired power generating unit lowers the limitation of climbing rate；
(8) generation of electricity by new energy unit output constrains
ΔPGw(t) >=0 w=1,2 ..., W, t=1,2 ..., T (16)
Described Optimized model in the daytime was to be generated electricity and the minimum object function of standby totle drilling cost with power system, with 15 minutes For the linear programming model of unit of account；
In the daytime the object function of Optimized model is as follows：
In formula, τ represents the period in Optimized model, τ=1,2 ... ..., Γ；
P’Gi(τ) represents active power output values of the fired power generating unit i in period τ；
FGi,t(P’Gi(τ)) it is thermal power unit operation cost；
Fi up(τ) provides up-regulation load stand-by cost for fired power generating unit；
Fi down(τ) is provided for fired power generating unit and is lowered load stand-by cost；
In the daytime all kinds of cost expressions in Optimized model are identical with Optimized model a few days ago；
In the daytime in Optimized model constraints, power flow equation, section tidal current constraint, generator output constraint, spare capacity Constraint, the constraint of fired power generating unit standby climbing rate, the constraint of fired power generating unit climbing rate, the constraint of generation of electricity by new energy unit output with it is a few days ago excellent Change model Unit Commitment state α that is identical, but being had determined in Optimized model a few days agoi(τ) and emergency duty arrangementIt will be kept in Optimized model in the daytime constant；
Described real-time optimization model is to adjust the minimum target letter of expense with power system thermoelectric generator active power output Number, with 15 minutes linear programming models for unit of account；
The object function of real-time optimization model is as follows：
In formula, P "Gi(δ) is fired power generating unit i future 15min active power output values；P’Gi(δ) is the thermal motor in optimizing in the daytime I is in period δ active power output value for group, and input parameter is used as in real-time optimization model；Crealt,iRepresent fired power generating unit i active power outputs Setup Cost coefficient；
In real-time optimization model constraints, power flow equation, section tidal current constraint, generator output constraint, spare capacity Constraint, the constraint of fired power generating unit standby climbing rate, the constraint of fired power generating unit climbing rate, the constraint of generation of electricity by new energy unit output with it is a few days ago excellent It is identical to change model, the Unit Commitment state α being had determined in Optimized model a few days agoi(δ) and emergency duty arrangementConstant, to be had determined in Optimized model in the daytime load stand-by arrangement will be kept in real-time optimization modelIt will be kept in real-time optimization model constant；
Step 2：By the performance parameter of every thermoelectric generator, cost coefficient, the conductance of system interconnection, admittance parameter And line transmission power limit, second day 24 hours each load bus predicted loads, generation of electricity by new energy units predict Force value is input to Optimized model a few days ago, and the start and stop state, active power output value, emergency duty of every thermoelectric generator are obtained after calculating Capacity, up-regulation reserve capacity for load variation in power, lower reserve capacity for load variation in power；
Step 3：By the performance parameter of every thermoelectric generator, cost coefficient, the conductance of system interconnection, admittance parameter And in line transmission power limit, setting time section each load bus amendment predicted load and generation of electricity by new energy unit The start and stop state and emergency reserve capacity for every thermoelectric generator that prediction power generating value, the step 1 of amendment obtain are input in the daytime Optimized model, the active power output value after every thermoelectric generator renewal, up-regulation reserve capacity for load variation in power are obtained after calculating, lowers load Spare capacity；
Step 4：By the performance parameter of every thermoelectric generator, cost coefficient, the conductance of system interconnection, admittance parameter And line transmission power limit, the predicted load that each load bus is corrected again in 15 minutes futures and generation of electricity by new energy Active power output value, up-regulation after every thermoelectric generator renewal that prediction power generating value, the step 2 that unit is corrected again obtain is negative Lotus spare capacity, lower reserve capacity for load variation in power, the start and stop state for every thermoelectric generator that step 1 obtains and emergency duty appearance Amount is input to real-time optimization model, and the active power output value that every thermoelectric generator updates again is obtained after calculating；Described is active Power generating value is used to instruct power system real time execution.
Further, described alternator performance parameter includes installed capacity, climbing rate and fuel type.
Further, the cost coefficient of described generator includes start-up and shut-down costs coefficient, operating cost coefficient, emergency duty Cost coefficient, load stand-by cost coefficient and active power output Setup Cost coefficient.
Compared with prior art, beneficial effects of the present invention are as follows：
The Multiple Time Scales of the present invention are generated electricity and standby combined optimization method, and error is predicted for new energy and load fluctuation With time scale reduce and the characteristics of constantly reduce, with system economy it is optimal for optimization aim from a few days ago, in the daytime, it is real-time three Time scale refines step by step, progressive fine analog power system generation schedule, on the one hand reduces fired power generating unit as consumption The wasting of resources caused by regenerative resource adjusts output repeatedly, improves Operation of Electric Systems economy；On the other hand, it is of the invention Optimization method employ with the ever-reduced rolling optimization model that becomes more meticulous of time scale, so as to ensure system reliability.
Brief description of the drawings
Fig. 1 is the optimization method flow chart of the present invention；
Fig. 2 is the electrical primary wiring diagram of implementation 1；
Fig. 3 is the shot and long term predicted value comparison diagram of wind power output；
Fig. 4 is generator G1 and G6 generated power output operation result figure；
Fig. 5 is generator G3 and G6 up-regulation load standby operation result figure.
Embodiment
The optimization method of the present invention is described further with reference to specific embodiment.
As shown in Fig. 2 the Multiple Time Scales of the present invention generate electricity, the electricity generation system being related to standby combined optimization method is total to There are 10 thermoelectric generators and a wind power plant, be distributed on 4 load buses.The wind power generating set prediction of wind power plant is contributed It is as shown in Figure 3 with actual power generating value.
A kind of Multiple Time Scales generate electricity and standby combined optimization method, it is characterised in that this method comprises the following steps：
Step 1：Build Optimized model, in the daytime Optimized model, real-time optimization model a few days ago；
Described Optimized model a few days ago be with power system generate electricity and the minimum object function of standby totle drilling cost, using hour as The MILP model of unit of account；
The object function of Optimized model is as follows a few days ago：
In formula, the fired power generating unit in i expression systems；
T represents the period of Optimized model a few days ago, t=1,2 ... ..., T；
FGi,t(PGi(t)) it is thermal power unit operation cost,
FGi,t(PGi(t))=ai·(PGi(t))2+bi·(PGi(t))+ci (2)
PGi(t) active power output values of the fired power generating unit i in period t is represented；
ai、bi、ciRepresent and fired power generating unit coa consumption rate coefficient correlation；
SUiFor fired power generating unit start shooting cost,
SUi=Si·αi(t)·(1-αi(t-1)) (3)
αi(t) startup-shutdown states of the unit i in the t periods is represented, if unit is started shooting, αi(t)=1, if compressor emergency shutdown, αi(t)=0；αi(t-1) startup-shutdown states of the unit i in the t-1 periods is represented, if unit is started shooting, αi(t-1)=1, if unit Shut down, then αi(t-1)=0；
SiRepresent fired power generating unit start cost coefficient；
Fi emergency(t) emergency duty cost is provided for fired power generating unit,
Represent emergency reserve capacities of the fired power generating unit i in period t；
Represent fired power generating unit i unit emergency duty cost coefficient；
Fi up(t) up-regulation load stand-by cost is provided for fired power generating unit,
Represent up-regulation reserve capacity for load variation in power of the fired power generating unit i in period t；
Represent fired power generating unit i unit up-regulation load stand-by cost coefficient；
Fi down(t) provided for fired power generating unit and lower load stand-by cost,
Represent downward reserve capacity for load variation in power of the fired power generating unit i in period t；
Represent that fired power generating unit i unit lowers load stand-by cost coefficient；
Optimized model constraints is as follows a few days ago：
(1) power flow equation
In formula, r represents load bus, r=r1,r2,...,R；
Wind power generating set in w expression systems；
PGw(t) maximum active power output predicted values of the wind power generating set w in period t is represented；
ΔPGw(t) wind power generating set w active power output dispatch value and its maximum active power output predicted value in period t are represented Difference；
Dr(t) a few days ago prediction load value of the r nodes in period t is represented；
B represents system interconnection parameter；
θ represents node power angle；
(2) section tidal current constrains
-Lmax≤Ptrans(t)≤LmaxT=1,2 ..., T (8)
In formula, Ptrans(t) the power flow momentum on interconnection is represented；
LmaxRepresent branch power transmission maximum；
(3) generator output constrains
In formula,Represent generating set i maximum generation capacity；
In formula,PGi Represent fired power generating unit i minimum generating capacity；
(4) spare capacity constrains
In formula,REup Expression system raises the standby minimum essential requirement of load；
REdown Expression system lowers the standby minimum essential requirement of load；
REemergency Represent the standby minimum essential requirement of systematic failures；
(5) the standby climbing rate constraint of fired power generating unit
In formula, Rampratei upExpression system raises the standby climbing demand of load；
Rampratei downExpression system lowers the standby climbing demand of load；
Rampratei emergencyRepresent the standby climbing demand of systematic failures；
(6) fired power generating unit minimum startup-shutdown constrains
In formula：DiFired power generating unit minimum downtime is represented, unit is hour；
OiThe fired power generating unit minimum available machine time is represented, unit is hour；
(7) fired power generating unit climbing rate constrains
In formula,Represent fired power generating unit up-regulation climbing rate limitation；
Represent that fired power generating unit lowers the limitation of climbing rate；
(8) wind power generating set units limits
ΔPGw(t) >=0 w=1,2 ..., W, t=1,2 ..., T (16)
Described Optimized model in the daytime was to be generated electricity and the minimum object function of standby totle drilling cost with power system, with 15 minutes For the linear programming model of unit of account；
In the daytime the object function of Optimized model is as follows：
In formula, τ represents the period in Optimized model, τ=1,2 ... ..., Γ；
P’Gi(τ) represents active power output values of the fired power generating unit i in period τ；
FGi,t(P’Gi(τ)) it is thermal power unit operation cost；
Fi up(τ) provides up-regulation load stand-by cost for fired power generating unit；
Fi down(τ) is provided for fired power generating unit and is lowered load stand-by cost；
In the daytime all kinds of cost expressions in Optimized model are identical with Optimized model a few days ago；
In the daytime in Optimized model constraints, power flow equation, section tidal current constraint, generator output constraint, spare capacity Constraint, the standby climbing rate constraint of fired power generating unit, the constraint of fired power generating unit climbing rate, wind power generating set units limits are with optimizing a few days ago Model is identical, but the Unit Commitment state α being had determined in Optimized model a few days agoi(τ) and emergency duty arrangementIt will be kept in Optimized model in the daytime constant；
Described real-time optimization model is to adjust the minimum target letter of expense with power system thermoelectric generator active power output Number, with 15 minutes linear programming models for unit of account；
The object function of real-time optimization model is as follows：
In formula, P "Gi(δ) is fired power generating unit i future 15min active power output values；P’Gi(δ) is the thermal motor in optimizing in the daytime I is in period δ active power output value for group, and input parameter is used as in real-time optimization model；Crealt,iRepresent fired power generating unit i active power outputs Setup Cost coefficient；
In real-time optimization model constraints, power flow equation, section tidal current constraint, generator output constraint, spare capacity Constraint, the standby climbing rate constraint of fired power generating unit, the constraint of fired power generating unit climbing rate, wind power generating set units limits are with optimizing a few days ago Model is identical, the Unit Commitment state α being had determined in Optimized model a few days agoi(δ) and emergency duty arrangement Constant, to be had determined in Optimized model in the daytime load stand-by arrangement will be kept in real-time optimization model It will be kept in real-time optimization model constant；
Step 2：By two performance parameters such as the installed capacity of every thermoelectric generator, climbing rates, start-up and shut-down costs coefficient, Four cost coefficients such as operating cost coefficient, emergency duty cost coefficient and load stand-by cost coefficient, the electricity of system interconnection Lead, admittance parameter and line transmission power limit, second day 24 hours each load bus predicted loads, wind-driven generators Group prediction power generating value is input to Optimized model a few days ago, obtained after calculating the start and stop state of every thermoelectric generator, active power output value, Emergency reserve capacity, up-regulation reserve capacity for load variation in power, lower reserve capacity for load variation in power；
Described active power output value, up-regulation reserve capacity for load variation in power, lower reserve capacity for load variation in power and provide ginseng for system participant Examine operation point data.
Step 3：By two performance parameters such as the installed capacity of every thermoelectric generator, climbing rate, operating cost coefficient, Two cost coefficients such as load stand-by cost coefficient, the conductance of system interconnection, admittance parameter and line transmission power limit, The prediction of the predicted load of each load bus amendment of every 15 minutes and wind power generating set amendment is contributed in following 3 hours The start and stop state and emergency reserve capacity for every thermoelectric generator that value, step 1 obtain are input to Optimized model in the daytime, calculate The active power output value after every thermoelectric generator renewal, up-regulation reserve capacity for load variation in power are obtained afterwards, lower reserve capacity for load variation in power.Now Obtained generating set active power output value and load standby configuration scheme is closer to Real-Time Scheduling result.
Step 4：Two performance parameters, the active power outputs such as the installed capacity of every thermoelectric generator, climbing rate are adjusted to This coefficient, the conductance of system interconnection, admittance parameter and line transmission power limit, each load bus in 15 minutes futures Every firepower hair that prediction power generating value, the step 2 that the predicted load and wind power generating set corrected again are corrected again obtain Every firepower that active power output value, up-regulation reserve capacity for load variation in power, downward reserve capacity for load variation in power, step 1 after motor renewal obtain The start and stop state and emergency reserve capacity of generator are input to real-time optimization model, and every thermoelectric generator is obtained after calculating again The active power output value of renewal；Described active power output value is used to instruct power system real time execution.
The standby optimum results of system operation are as shown in table 1.Wherein, the stand-by cost of generator 7/9/10 is higher, so at this Do not there is provided in the scheduling of embodiment standby.Up-regulation load stand-by arrangement is adjusted from table 1 it follows that optimizing in the daytime It is whole.
The standby optimum results of the system operation of table 1
Fig. 3 is the shot and long term predicted value comparison diagram of wind power output；Fig. 4 is that fired power generating unit G1 and G6 optimizes in Multiple Time Scales In, the adjustment curve of active power output.As shown in Figure 4, the second day unit operation result of Optimized model simulation a few days ago is more pressed close to Actual value, a few days ago optimum results accurately time day operation reference point can be provided for Generation Side and control centre.In addition, by Fig. 3 can be seen that in T045-T070 periods, Wind turbines in short-term maximum active power output predicted value be higher than it is a few days ago maximum active Output predicted value, Real-Time Scheduling ought to reduce fired power generating unit and contribute to maintain the system equilibrium of supply and demand to dissolve more wind-powered electricity generations.By Fig. 4 can be seen that the result of unit output in the daytime of two generating sets of G1 and G6 all decreases in the period, and generator In the daytime Optimal Curve is more nearly the actual power generating value of unit to group active power output, can prove that Optimized model can be generator in the daytime Adjustment, which provides, in real time more accurately runs reference point.Similarly, as shown in Figure 5, the up-regulation that Optimized model simulates to obtain a few days ago is born Lotus stand-by arrangement is more pressed close to Optimized model result in the daytime, and both errors mainly predict error and load by wind power output Prediction error causes, the more economy compared with a few days ago of stand-by arrangement in the daytime.
It follows that Multiple Time Scales generate electricity with standby Optimized model by constantly progressive optimization, can be that system is joined The operation reference point constantly corrected is provided with person, reduces unit and adjusts pressure and the in real time wave of resource caused by adjustment in real time Take, therefore improve the economy of system, at the same time, refinement, progressive Optimized model further increase and be step by step The reliability of system.

Claims (3)

1. a kind of Multiple Time Scales generate electricity and standby combined optimization method, it is characterised in that this method comprises the following steps：
Step 1：Build Optimized model, in the daytime Optimized model, real-time optimization model a few days ago；
Described Optimized model a few days ago is to be generated electricity and the minimum object function of standby totle drilling cost with power system, using hour as calculating The MILP model of unit；
The object function of Optimized model is as follows a few days ago：
<mrow> <mi>M</mi> <mi>i</mi> <mi>n</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <mo>&amp;lsqb;</mo> <msub> <mi>F</mi> <mrow> <mi>G</mi> <mi>i</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>SU</mi> <mi>i</mi> </msub> <mo>+</mo> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
In formula, the fired power generating unit in i expression systems；
T represents the period of Optimized model a few days ago, t=1,2 ... ..., T；
FGi,t(PGi(t)) it is thermal power unit operation cost,
FGi,t(PGi(t))=ai·(PGi(t))2+bi·(PGi(t))+ci (2)
PGi(t) active power output values of the fired power generating unit i in period t is represented；
ai、bi、ciRepresent and fired power generating unit coa consumption rate coefficient correlation；
SUiFor fired power generating unit start shooting cost,
SUi=Si·αi(t)·(1-αi(t-1)) (3)
αi(t) startup-shutdown states of the unit i in the t periods is represented, if unit is started shooting, αi(t)=1, if compressor emergency shutdown, αi(t) =0；αi(t-1) startup-shutdown states of the unit i in the t-1 periods is represented, if unit is started shooting, αi(t-1)=1, if compressor emergency shutdown, Then αi(t-1)=0；
SiRepresent fired power generating unit start cost coefficient；
Fi emergency(t) emergency duty cost is provided for fired power generating unit,
<mrow> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>C</mi> <mi>i</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msubsup> <mo>&amp;CenterDot;</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
Represent emergency reserve capacities of the fired power generating unit i in period t；
Represent fired power generating unit i unit emergency duty cost coefficient；
Fi up(t) up-regulation load stand-by cost is provided for fired power generating unit,
<mrow> <msubsup> <mi>E</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>C</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mo>&amp;CenterDot;</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
Represent up-regulation reserve capacity for load variation in power of the fired power generating unit i in period t；
Represent fired power generating unit i unit up-regulation load stand-by cost coefficient；
Fi down(t) provided for fired power generating unit and lower load stand-by cost,
<mrow> <msubsup> <mi>E</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>C</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mo>&amp;CenterDot;</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
Represent downward reserve capacity for load variation in power of the fired power generating unit i in period t；
Represent that fired power generating unit i unit lowers load stand-by cost coefficient；
Optimized model constraints is as follows a few days ago：
(1) power flow equation
<mrow> <munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>&amp;Element;</mo> <mi>r</mi> </mrow> </munder> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>w</mi> <mo>&amp;Element;</mo> <mi>r</mi> </mrow> </munder> <mrow> <mi>w</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>W</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>w</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>&amp;Delta;P</mi> <mrow> <mi>G</mi> <mi>w</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>D</mi> <mi>r</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mi>R</mi> </munder> <mi>B</mi> <mi>&amp;theta;</mi> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>T</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
In formula, r represents load bus, r=r1,r2,...,R；
Generation of electricity by new energy unit in w expression systems；
PGw(t) maximum active power output predicted values of the generation of electricity by new energy unit w in period t is represented；
ΔPGw(t) generation of electricity by new energy unit w active power output dispatch value and its maximum active power output prediction value difference in period t are represented Value；
Dr(t) a few days ago prediction load value of the r nodes in period t is represented；
B represents system interconnection parameter；
θ represents node power angle；
(2) section tidal current constrains
-Lmax≤Ptrans(t)≤LmaxT=1,2 ..., T (8)
In formula, Ptrans(t) the power flow momentum on interconnection is represented；
LmaxRepresent branch power transmission maximum；
(3) generator output constrains
<mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>+</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msubsup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> <mo>&amp;le;</mo> <mover> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>&amp;CenterDot;</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>I</mi> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>T</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> <mo>,</mo> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <munder> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mo>&amp;OverBar;</mo> </munder> <mo>&amp;CenterDot;</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>I</mi> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>T</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
In formula,Represent generating set i maximum generation capacity；
In formula,PGi Represent fired power generating unit i minimum generating capacity；
(4) spare capacity constrains
<mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <munder> <mrow> <msup> <mi>RE</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msup> </mrow> <mo>&amp;OverBar;</mo> </munder> <mo>&amp;CenterDot;</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
<mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <munder> <mrow> <msup> <mi>RE</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msup> </mrow> <mo>&amp;OverBar;</mo> </munder> <mo>&amp;CenterDot;</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
<mrow> <mtable> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <munder> <mrow> <msup> <mi>RE</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msup> </mrow> <mo>&amp;OverBar;</mo> </munder> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>I</mi> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>T</mi> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
In formula,REup Expression system raises the standby minimum essential requirement of load；
REdown Expression system lowers the standby minimum essential requirement of load；
REemergency Represent the standby minimum essential requirement of systematic failures；
(5) the standby climbing rate constraint of fired power generating unit
<mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msup> <msub> <mi>Ramprate</mi> <mi>i</mi> </msub> <mrow> <mi>e</mi> <mi>m</mi> <mi>e</mi> <mi>r</mi> <mi>g</mi> <mi>e</mi> <mi>n</mi> <mi>c</mi> <mi>y</mi> </mrow> </msup> <mo>&amp;CenterDot;</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
<mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msup> <msub> <mi>Ramprate</mi> <mi>i</mi> </msub> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msup> <mo>&amp;CenterDot;</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
<mrow> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msubsup> <mi>RE</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msup> <msub> <mi>Ramprate</mi> <mi>i</mi> </msub> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msup> <mo>&amp;CenterDot;</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>I</mi> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>T</mi> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>
In formula, Rampratei upExpression system raises the standby climbing demand of load；
Rampratei downExpression system lowers the standby climbing demand of load；
Rampratei emergencyRepresent the standby climbing demand of systematic failures；
(6) fired power generating unit minimum startup-shutdown constrains
<mrow> <mtable> <mtr> <mtd> <mrow> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> <mo>+</mo> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mi>&amp;lambda;</mi> </mrow> <mo>)</mo> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mi>&amp;lambda;</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>)</mo> <mo>&amp;le;</mo> <mn>1</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>&amp;lambda;</mi> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>D</mi> <mi>i</mi> </msub> <mo>-</mo> <mn>1</mn> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow>
<mrow> <mtable> <mtr> <mtd> <mrow> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>)</mo> <mo>+</mo> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mi>&amp;lambda;</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>-</mo> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>(</mo> <mrow> <mi>t</mi> <mo>+</mo> <mi>&amp;lambda;</mi> </mrow> <mo>)</mo> <mo>)</mo> <mo>&amp;le;</mo> <mn>1</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>&amp;ForAll;</mo> <mi>&amp;lambda;</mi> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>O</mi> <mi>i</mi> </msub> <mo>-</mo> <mn>1</mn> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>14</mn> <mo>)</mo> </mrow> </mrow>
In formula：DiFired power generating unit minimum downtime is represented, unit is hour；
OiThe fired power generating unit minimum available machine time is represented, unit is hour；
(7) fired power generating unit climbing rate constrains
<mrow> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <mover> <mrow> <msubsup> <mi>&amp;Delta;P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> </mrow> <mo>&amp;OverBar;</mo> </mover> </mrow>
<mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <mover> <mrow> <msubsup> <mi>&amp;Delta;P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> </mrow> <mo>&amp;OverBar;</mo> </mover> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>I</mi> <mo>,</mo> <mi>t</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>T</mi> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow>
In formula,Represent fired power generating unit up-regulation climbing rate limitation；
Represent that fired power generating unit lowers the limitation of climbing rate；
(8) generation of electricity by new energy unit output constrains
ΔPGw(t) >=0 w=1,2 ..., W, t=1,2 ..., T (16)
Described Optimized model in the daytime is to be generated electricity and the minimum object function of standby totle drilling cost with power system, in terms of being within 15 minutes Calculate the linear programming model of unit；
In the daytime the object function of Optimized model is as follows：
<mrow> <mi>M</mi> <mi>i</mi> <mi>n</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>&amp;tau;</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>&amp;Gamma;</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <mo>&amp;lsqb;</mo> <msub> <mi>F</mi> <mrow> <mi>G</mi> <mi>i</mi> <mo>,</mo> <mi>&amp;tau;</mi> </mrow> </msub> <mrow> <mo>(</mo> <msubsup> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> <mo>&amp;prime;</mo> </msubsup> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msubsup> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> <mo>+</mo> <msubsup> <mi>F</mi> <mi>i</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow>
In formula, τ represents the period in Optimized model, τ=1,2 ... ..., Γ；
P′Gi(τ) represents active power output values of the fired power generating unit i in period τ；
FGi,t(P′Gi(τ)) it is thermal power unit operation cost；
Fi up(τ) provides up-regulation load stand-by cost for fired power generating unit；
Fi down(τ) is provided for fired power generating unit and is lowered load stand-by cost；
In the daytime all kinds of cost expressions in Optimized model are identical with Optimized model a few days ago；
In the daytime in Optimized model constraints, power flow equation, section tidal current constrain, generator output constrains, spare capacity constrains, The standby climbing rate constraint of fired power generating unit, the constraint of fired power generating unit climbing rate, the constraint of generation of electricity by new energy unit output are with optimizing mould a few days ago Type is identical, but the Unit Commitment state α being had determined in Optimized model a few days agoi(τ) and emergency duty arrangement It will be kept in Optimized model in the daytime constant；
Described real-time optimization model is to adjust the minimum object function of expense with power system thermoelectric generator active power output, with 15 minutes linear programming models for unit of account；
The object function of real-time optimization model is as follows：
<mrow> <mi>M</mi> <mi>i</mi> <mi>n</mi> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <mo>|</mo> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>a</mi> <mi>l</mi> <mi>t</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <msubsup> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> <mrow> <mo>&amp;prime;</mo> <mo>&amp;prime;</mo> </mrow> </msubsup> <mo>(</mo> <mi>&amp;delta;</mi> <mo>)</mo> <mo>-</mo> <msubsup> <mi>P</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> <mo>&amp;prime;</mo> </msubsup> <mo>(</mo> <mi>&amp;delta;</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>|</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>18</mn> <mo>)</mo> </mrow> </mrow>
In formula, P "Gi(δ) is fired power generating unit i future 15min active power output values；P′Gi(δ) is that fired power generating unit i exists in optimizing in the daytime Period δ active power output value, input parameter is used as in real-time optimization model；Crealt,iRepresent the adjustment of fired power generating unit i active power outputs Cost coefficient；
In real-time optimization model constraints, power flow equation, section tidal current constrain, generator output constrains, spare capacity constrains, The standby climbing rate constraint of fired power generating unit, the constraint of fired power generating unit climbing rate, the constraint of generation of electricity by new energy unit output and Optimized model a few days ago Unit Commitment state α that is identical, being had determined in Optimized model a few days agoi(δ) and emergency duty arrangementReal-time Constant, to be had determined in Optimized model in the daytime load stand-by arrangement will be kept in Optimized model It will be kept in real-time optimization model constant；
Step 2：By the performance parameter of every thermoelectric generator, cost coefficient, the conductance of system interconnection, admittance parameter and Line transmission power limit, 24 hours each load bus predicted loads of second day, generation of electricity by new energy unit prediction power generating value Optimized model a few days ago is input to, the start and stop state, active power output value, emergency duty that every thermoelectric generator is obtained after calculating are held Amount, up-regulation reserve capacity for load variation in power, lower reserve capacity for load variation in power；
Step 3：By the performance parameter of every thermoelectric generator, cost coefficient, the conductance of system interconnection, admittance parameter and The predicted load of each load bus amendment and generation of electricity by new energy unit amendment in line transmission power limit, setting time section Prediction power generating value, the obtained start and stop state of every thermoelectric generator of step 1 and emergency reserve capacity be input to and optimize in the daytime Model, it is standby that the active power output value after every thermoelectric generator renewal, up-regulation reserve capacity for load variation in power, downward load are obtained after calculating Capacity；
Step 4：By the performance parameter of every thermoelectric generator, cost coefficient, the conductance of system interconnection, admittance parameter and Line transmission power limit, each load bus is corrected again in 15 minutes futures predicted load and generation of electricity by new energy unit Active power output value, up-regulation load after every thermoelectric generator renewal that prediction power generating value, the step 2 corrected again obtain is standby The start and stop state and emergency reserve capacity of the every thermoelectric generator obtained with capacity, downward reserve capacity for load variation in power, step 1 are defeated Enter and the active power output value that every thermoelectric generator updates again to real-time optimization model, is obtained after calculating；Described active power output It is worth for instructing power system real time execution.
2. Multiple Time Scales according to claim 1 generate electricity and standby combined optimization method, the performance of described generator Parameter includes installed capacity, climbing rate and fuel type.
3. Multiple Time Scales according to claim 1 generate electricity and standby combined optimization method, the cost of described generator Coefficient includes start-up and shut-down costs coefficient, operating cost coefficient, emergency duty cost coefficient, load stand-by cost coefficient and active power output Setup Cost coefficient.
CN201710846018.7A 2017-09-19 2017-09-19 A kind of Multiple Time Scales generate electricity and standby combined optimization method Pending CN107769266A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710846018.7A CN107769266A (en) 2017-09-19 2017-09-19 A kind of Multiple Time Scales generate electricity and standby combined optimization method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710846018.7A CN107769266A (en) 2017-09-19 2017-09-19 A kind of Multiple Time Scales generate electricity and standby combined optimization method

Publications (1)

Publication Number Publication Date
CN107769266A true CN107769266A (en) 2018-03-06

Family

ID=61265560

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710846018.7A Pending CN107769266A (en) 2017-09-19 2017-09-19 A kind of Multiple Time Scales generate electricity and standby combined optimization method

Country Status (1)

Country Link
CN (1) CN107769266A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109617137A (en) * 2019-01-14 2019-04-12 广东电力交易中心有限责任公司 Consider the electric system output distribution method of flexible modulation unit generation share constraint
CN110034587A (en) * 2019-04-22 2019-07-19 广东电网有限责任公司 A kind of Optimization Scheduling
CN110611337A (en) * 2019-09-30 2019-12-24 国家电网公司华中分部 Power supply reliability-based power system energy and standby combined scheduling method
CN116613750A (en) * 2023-07-18 2023-08-18 山东大学 Integrated scheduling method, system, terminal equipment and medium for power system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102289566A (en) * 2011-07-08 2011-12-21 浙江大学 Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode
CN106992556A (en) * 2017-05-24 2017-07-28 南方电网科学研究院有限责任公司 Optimal scheduling method based on hybrid power supply multi-time scale complementation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102289566A (en) * 2011-07-08 2011-12-21 浙江大学 Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode
CN106992556A (en) * 2017-05-24 2017-07-28 南方电网科学研究院有限责任公司 Optimal scheduling method based on hybrid power supply multi-time scale complementation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
XINHONG YOU等: "Optimal day-ahead and intra-day scheduling of energy and operating reserve considering fluctuating wind power", 《IEEE》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109617137A (en) * 2019-01-14 2019-04-12 广东电力交易中心有限责任公司 Consider the electric system output distribution method of flexible modulation unit generation share constraint
CN110034587A (en) * 2019-04-22 2019-07-19 广东电网有限责任公司 A kind of Optimization Scheduling
CN110611337A (en) * 2019-09-30 2019-12-24 国家电网公司华中分部 Power supply reliability-based power system energy and standby combined scheduling method
CN116613750A (en) * 2023-07-18 2023-08-18 山东大学 Integrated scheduling method, system, terminal equipment and medium for power system
CN116613750B (en) * 2023-07-18 2023-10-13 山东大学 Integrated scheduling method, system, terminal equipment and medium for power system

Similar Documents

Publication Publication Date Title
CN111555281B (en) Method and device for simulating flexible resource allocation of power system
CN107240933B (en) Wind-fire coordinated rolling scheduling method considering wind power characteristics
CN108039737B (en) Source-grid-load coordinated operation simulation system
CN107634547B (en) Power output control method of electric combined system based on new energy output prediction error
CN106485352A (en) A kind of multiclass power supply generation schedule combination decision-making method
CN107769266A (en) A kind of Multiple Time Scales generate electricity and standby combined optimization method
CN110212579A (en) A kind of wind-water-fire joint robust Unit Combination method
CN104143839B (en) Wind power plant cluster based on power prediction limits active power distribution method of exerting oneself
CN114301081B (en) Micro-grid optimization method considering storage battery energy storage life loss and demand response
CN109510238B (en) Coordinated dispatching unit combination method for efficiently solving hydroelectric power, thermal power and wind power
CN109992818A (en) Unit combination model with large-scale wind power participating in primary frequency modulation and solving method
CN109787217B (en) Standby clearing method based on wind power multi-state model and opportunity cost correction
CN116402210A (en) Multi-objective optimization method, system, equipment and medium for comprehensive energy system
CN112994011B (en) Multi-source power system day-ahead optimal scheduling method considering voltage risk constraint
Lopes et al. Metaheuristic methods applied to the pumps and turbines configuration design of water pumped storage systems
CN114465226A (en) Method for establishing multi-level standby acquisition joint optimization model of power system
JP6705319B2 (en) Integrated control device, integrated control system, integrated control method, and integrated control program
Baziar et al. Evolutionary algorithm-based adaptive robust optimization for AC security constrained unit commitment considering renewable energy sources and shunt FACTS devices
Zhang et al. Research on frequency regulation strategy based on model predictive control for wind-hydro-storage complementary microgrid
CN115296345B (en) Start-stop-output-standby combined optimization method and device for gas generator set
CN115169128A (en) Comprehensive energy system unit combination method and system considering network dynamic characteristics
CN114819573A (en) Day-ahead scheduling method and device of new energy system and computer equipment
Su et al. Quantification and Evaluation of Power System Flexibility under Different Time Scales
Lu et al. Unit commitment of power system with wind power and photovoltaic considering frequency safety constraint
CN118316034B (en) Method and device for dynamically calculating carbon emission factors of multi-node system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20180306