CN109390981A - The sending end power grid Unit Combination progress control method of new energy participation electric quantity balancing - Google Patents
The sending end power grid Unit Combination progress control method of new energy participation electric quantity balancing Download PDFInfo
- Publication number
- CN109390981A CN109390981A CN201811459376.3A CN201811459376A CN109390981A CN 109390981 A CN109390981 A CN 109390981A CN 201811459376 A CN201811459376 A CN 201811459376A CN 109390981 A CN109390981 A CN 109390981A
- Authority
- CN
- China
- Prior art keywords
- unit
- power
- new energy
- units
- constraints
- 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.)
- Granted
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
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Control Of Eletrric Generators (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to the sending end power grid Unit Combination progress control methods that a kind of new energy participates in electric quantity balancing, comprising the following steps: 1) obtains the booting prioritised list of prediction the power output confidence interval and conventional electric power generation unit of new energy;2) it introduces spare unit pond and abandons motor group pond, while considering that new energy participates in balance, spinning reserve constraint and minimum start-off time constraints, control set state;3) set state obtained using step 2) is adjusted particle position based on the power-balance for considering unit climbing rate, obtains the optimal output of particle swarm algorithm as the input of particle swarm algorithm, controls Unit Combination operating status according to the optimal output.Compared with prior art, the present invention has many advantages, such as to improve the economy of system operation.
Description
Technical field
The present invention relates to electric system automation fields, and the sending end of electric quantity balancing is participated in more particularly, to a kind of new energy
Power grid Unit Combination progress control method.
Background technique
Optimization of Unit Commitment By Improved (UC problem) generally refers to meeting system load demand, spare capacity and minimum start-stop time
Etc. determine that the start and stop of each unit of day part in research cycle and power output arrange under constraint conditions, keep total power generation expense minimum, number
Learning model is substantially higher-dimension, discrete, non-convex mixed-integer nonlinear programming model, is not easy to obtain theoretic optimal solution.With
The expansion of system scale, the optimality of the increase of model solution spatial complex degree, model solution time and solution decline therewith, have
Therefore a little methods can not even acquire feasible solution.Reasonably optimizing Unit Combination can bring distinct economic to electric system, right
Bulk power grid economy reliability is promoted to be of great significance.
Domestic scholars have carried out extensive research to UC problem, propose many method for solving: priority method, Dynamic Programming
Method, Lagrangian Relaxation, intelligent optimization algorithm, mixed integer programming approach etc..Li Jinghua, Lan Fei " protecting electrical power system with
Control " " the extension priority method for being suitable for Optimization of Unit Commitment By Improved " that delivers on (2010,38 (02): 1-7) define unit
Utilization coefficient UUR (Unit Utilization Ratio) optimization unit priority, and introduce state modulator unit group
It closes the scale of neighborhood and taking strategy to be adjusted Unit Combination makes it meet all constraints.Zhai Junyi, Ren Jianwen, Lee are whole etc.
" a kind of dimensionality reduction solution unit delivered on " North China Electric Power University's journal (natural science edition) " (2016,43 (01): 32-38)
The dual particle swarm optimization algorithm of combinatorial problem " by the optimization of entire dispatching cycle be converted into each scheduling instance successively,
It is separately optimized, i.e., will be converted into the optimization to row vector to the optimization of matrix, reduce and solve dimension.In conjunction with discrete and continuous particle
Group's (particle swarm optimization, PSO) algorithm, respectively obtains current scheduling moment optimal Unit Combination shape
State and corresponding optimum load dispatch.Deng Jun, Wei Hua, Li Jinghua etc. " Proceedings of the CSEE " (2015,35 (11):
" a kind of Unit Combination mixed integer linear programming model containing four class 0-1 variables " delivered on 2770-2778) is auxiliary by introducing
Help variable indicate cold start, propose a kind of linear expression of starting expense, at the same enhance MILP model terseness and
Compactedness;It is limited using Ramp Rate and minimum runing time, proposes new unit output constraint expression, greatly have compressed machine
The feasible zone of group power output, further enhances compactedness, improves model solution efficiency.Zhang Shu, Hu Zechun, Song Yonghua etc. exist
" the security constraint based on network loss factor iteration delivered on " Proceedings of the CSEE " (2012,32 (07): 76-82+194)
Unit Combination algorithm " the SCUC problem for fixing the network loss factor is first solved in each iteration, the operating status of unit is acquired, is then carried out
AC power flow calculates, and the network loss factor is updated, into next iteration.For the network loss factor oscillation problem being likely to occur, propose
The method that SCUC and economic load dispatching combine selects the corresponding lesser Unit Commitment state of cost of electricity-generating, it is excellent to carry out economic load dispatching
Change and the network loss factor iterates to calculate, until algorithmic statement.In document above, priority method solves extensive UC problem precision not
Height, intelligent algorithm is computationally intensive, solving speed is slow, easily limits into locally optimal solution, and mixed integer programming class Method Modeling is complicated, counts
Calculation amount is big, convergence is slow, it is also possible to cause solver that can not acquire feasible solution.In patent, Zhang Jingrui, Lin Shuan, the hair such as Qiu Weixia
The patent of invention " the Unit Commitment optimization method for considering ramping rate constraints " of bright people's application is in discrete particle cluster frame
Difference acceleration technique is introduced in frame to improve solving speed, infeasible individual is repaired to improve feasibility, and using with
The equivalent λ iterative method of machine carries out sharing of load and handles ramping rate constraints.The inventors such as Cai Qiuna, Liu Sijie, Yang Yun application
Patent of invention " a method of Unit Combination is arranged based on power plant sequence coefficient " using sequence coefficient, and consider that electric power is flat
Weighing apparatus, security constraint arrange Unit Combination by day, and Unit Combination can be arranged for power dispatching station, carries out monthly electricity regulation.
A kind of patent of invention " energy coordination optimization Unit Combination method " of the inventors such as Liu Fang, Pan Yi, Zhou Jingyang application considers wind-powered electricity generation
Power output confidence interval, the operating parameter and operation characteristic of the operating parameter of fired power generating unit and consumption coal characteristic and pump-storage generator
Mixed integer non-linear programming problems model is established to be solved.Li Lili, fourth is proper, the inventors such as Geng Jian application patent of invention " in
Long-term Unit Combination optimization method " it examines and is established according to the electric network model of actual electric network with unit generation amount and Expected energy deviation most
The small medium-term and long-term security constraint Unit Combination model for target;Unit is calculated within dispatching cycle using mixed integer programming approach
Then start and stop state, rate of load condensate and the active power output of peak load period in each day pass through the iteration of optimisation technique and Security Checking,
It is final to obtain the medium-term and long-term Unit Combination scheme for meeting power grid security.The above patent all only with single priority method or
Person's intelligent algorithm, or call directly mature Mathematical Planning solver and solve UC problem, thus above-mentioned class cannot be solved very well
Type method is applied to existing computational efficiency low inherent shortcoming when extensive UC problem.Therefore the Unit Combination of large scale system
The efficient solution of problem is worth further investigation.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of new energy to participate in electricity
Measure the sending end power grid Unit Combination progress control method of balance.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of new energy participates in the sending end power grid Unit Combination progress control method of electric quantity balancing, comprising the following steps:
1) the booting prioritised list of prediction the power output confidence interval and conventional electric power generation unit of new energy is obtained;
2) it introduces spare unit pond and abandons motor group pond, while considering that new energy participates in balance, spinning reserve constraint and most
Small start-off time constraints control set state;
3) set state obtained using step 2) is flat based on the power for considering unit climbing rate as the input of particle swarm algorithm
Weighing apparatus adjustment particle position, obtains the optimal output of particle swarm algorithm, controls Unit Combination operating status according to the optimal output.
Further, the conventional electric power generation unit includes fired power generating unit and Hydropower Unit, is obtained based on sequence reference factor
The booting prioritised list of fired power generating unit, the sequence reference factor include adjustable generating set coal consumption index factor, starting
Cost reference factor and the power output upper limit reference factor obtain different fire according to the difference of the parameter selection of sequence reference factor
Motor group booting prioritised list;
The Hydropower Unit obtains corresponding booting prioritised list according to regulation performance.
Further, generating set booting priority is determined after respectively sequence reference factor is combined by following formula:
F=index1, i+index2, i+index3, i
In formula, index1, i、index2, i、index3, iRespectively press adjustable generating set coal consumption index factor, start-up cost
The ranking of unit i after reference factor and the power output upper limit reference factor sort from small to large, f value is smaller, and corresponding unit more preferentially opens
It is dynamic.
Further, the motor group pond of abandoning includes abandoning the Hydropower Unit of water and just in abandonment, the new energy of abandoning light
Unit;
Spare unit pond includes the fired power generating unit for being currently at hot stand-by duty.
Further, the step 2) specifically includes the following steps:
A1) according to load prediction curve, judge whether subsequent period load prediction increment is positive, if so, thening follow the steps
A2), if it is not, thening follow the steps a3);
A2) be based on the booting prioritised list, successively control abandon motor group pond, new energy unit, spare unit pond,
Corresponding unit starting in fired power generating unit executes step a4 until meeting the growth of load and spinning reserve constraint);
A3 it) is based on the booting prioritised list, fired power generating unit is successively controlled, many years regulation Hydropower Unit, adjusts in year
Hydropower Unit, season adjust Hydropower Unit, new energy unit, the corresponding unit adjusted in Hydropower Unit day and shut down, until meeting rotation
Turn Reserve Constraint and load is reduced, execute step a4);
A4) judge whether that the set state of all periods controls to finish, if so, thening follow the steps 3), if it is not, then returning
Step a1).
Further, the step a2) it specifically includes:
201) under channel constraints, unit starting is selected from abandoning in motor group pond;
202) judge whether meet the growth of load and spinning reserve constraint after abandoning the corresponding unit starting in motor group pond, if so,
Then follow the steps a4), if it is not, thening follow the steps 203);
203) under channel constraints, new energy unit is started according to prediction power output confidence interval;
204) it whether is able to satisfy the growth of load and spinning reserve constraint after judging new energy unit starting, if so, executing
Step a4), if it is not, thening follow the steps 205);
205) fired power generating unit starting is selected from spare unit pond;
206) it whether is able to satisfy the growth of load and spinning reserve constraint after judging fired power generating unit starting, if so, executing step
Rapid a4), if it is not, thening follow the steps 207);
207) limited load control is carried out, and starts fired power generating unit, increases the unit quantity in spare unit pond, executes step
a4)。
Further, the step a3) it specifically includes:
301) after insignificant support fired power generating unit is shut down in judgement, spinning reserve constraint whether is met in 24 hours and load subtracts
It is few, if so, step 302) is executed after shutting down qualified unit, if it is not, thening follow the steps 302);
302) after many years regulation Hydropower Unit is shut down in judgement, spinning reserve constraint whether is met in 3 hours and load is reduced,
If so, step 303) is executed after shutting down qualified unit, if it is not, thening follow the steps 303);
303) after year adjusting Hydropower Unit is shut down in judgement, spinning reserve constraint whether is met in 3 hours and load is reduced, if
It is execution step 304) after then shutting down qualified unit, if it is not, thening follow the steps 304);
304) after season adjusting Hydropower Unit is shut down in judgement, spinning reserve constraint whether is met in 3 hours and load is reduced, if
It is execution step 305) after then shutting down qualified unit, if it is not, thening follow the steps 305);
305) after new energy unit is shut down in judgement, if meet load and reduce, if so, after shutting down qualified unit
Step 306) is executed, if it is not, thening follow the steps 306);
306) after day adjusting Hydropower Unit is shut down in judgement, if meet load and reduce, if so, shutting down qualified machine
Step a4 is executed after group), if it is not, thening follow the steps a4).
Further, the objective function that the particle swarm algorithm uses are as follows:
Wherein, number of segment when T is total, Nh、Ns、NxIt is thermoelectricity, water power, new energy unit number, PH respectivelyI, tIt is fired power generating unit i
In the power output of period t, PSI, tIt is power output of the Hydropower Unit i in period t, PXI, tIt is power output of the new energy unit i in period t, uI, t
It is start and stop state of the unit i in period t, is indicated with 0 or 1, FHI, t(PHI, t) it is operating cost of the fired power generating unit i in period t,
STiIt is the start-up and shut-down costs of fired power generating unit i, FSI, t(PSI, t) it is operating cost of the Hydropower Unit i in period t, FXI, t(PXI, t) be
Operating cost of the new energy unit i in period t;
The constraint condition of use includes system power Constraints of Equilibrium, the constraint of unit output bound, spinning reserve constraint, machine
Group Climing constant and startup and shutdown of units time-constrain.
It is further, described based on the power-balance adjustment particle position for considering unit climbing rate specifically:
221) initialization period t=1;
222) all unit output summation SUM of calculation interval ttAnd the power output range PH (i) and PL (i) of each unit i,
PH (i) is highest power output, and PL (i) is minimum output;
223) a booting list is established, which, which is stored with, is in the unit of open state in period t;
224) SUM is judged whether there ist< DtAnd count > 0, if so, thening follow the steps 225), if it is not, thening follow the steps
226), wherein DtIndicate that the workload demand of period t, count indicate unit number in booting list;
225) unit i is chosen from the booting list, enables PI, t=PH (i), recalculates SUMt, judge whether there is
SUMt< Dt, if so, thening follow the steps 226), if it is not, then deleting unit i from booting list, update count, return step
224);
226) booting list is re-established, count is updated;
227) SUM is judged whether there ist> DtAnd count > 0, if so, thening follow the steps 228), if it is not, thening follow the steps
229);
228) unit i is chosen from the booting list, enables PI, t=PL (i), recalculates SUMt, judge whether there is
SUMt< Dt, if so, executing step 229), if it is not, then deleting unit i from booting list, update count;
If 229) SUM at this timet≠DtAnd count > 0, then enable PI, t=min { PI, t-(SUMt-Dt), PH (i) }, otherwise,
PI, t=max { PI, t, PL (i) };
230) step 222) -229 is repeated), until t=T.
Further, the power output range PH (i) and PL (i) are obtained in the following manner:
To each unit i in each period t, if uit=1, ui(t-1)=0, then PH (i)=min { PI, max, RUi, PL (i)
=max { PI, min, RDi};
If uit=1, ui(t-1)=1, then PH (i)=min { PI, m α x, PI, t-1+RUi, PL (i)=max { PI, min, PI, t-1-
RDi};
If t=1, PH (i)=PI, max, PL (i)=PI, min;
If uit=1, ui(t+1)=0, then PH (i)=PL (i)=PI, min;
If uit=1, ui(t+1)=1, then PH (i)=min { PH (i), PI, t+1-RDi, PL (i)=max { PL (i), PI, t+1-
RUi};
Wherein, RDi、RUiIndicate the upper and lower creep speed of unit.
Compared with prior art, the present invention have with following the utility model has the advantages that
1, the present invention considers that the new energy such as wind-powered electricity generation, solar energy participate in systematic electricity electric quantity balancing, control as normal power supplies
Precision is higher.
2, abandoning motor group pond and spare unit pond, optimization system balance of electric power and ener process, on the one hand, so that the mistake are utilized
Journey more meets reality, on the other hand, improves the economy of system operation.
3, according to Hydropower Unit regulation performance, fired power generating unit energy consumption index, priority row is carried out to conventional electric power generation unit
Sequence.
4, single for tradition PL method sequence index, it is unable to the deficiency of thoroughly evaluating unit operating cost, present invention introduces
Adjustable generating set coal consumption, unit maximum output, three indexs of unit starting cost, the economy for reflecting generating set comprehensively,
The economy of Unit Commitment plan can be improved in improved Unit Economic type sequence index, saves coal-fired cost.
5, the present invention carries out prioritizing to conventional electric power generation unit, system economical operation type can be improved, energy conservation subtracts
Row.
6, it first arranges Unit Commitment to meet spinning reserve different from traditional PL method, then adjusts set state and meet minimum start and stop
Time, the present invention consider the two constraints simultaneously, the preferential unlatching lesser unit of operating cost is better achieved.
7, for the more difficult deficiency for meeting this equality constraint of power-balance constraint of original PSO, a kind of consideration unit is proposed
The power-balance adjustable strategies of climbing rate guarantee the multifarious of particle while avoiding rapidly converging to locally optimal solution, improve
The quality of last solution.
8, the two-phase optimization method that the present invention uses improves large scale system Unit Combination computational efficiency and calculates essence
Degree.
Detailed description of the invention
Fig. 1 is flow diagram of the invention;
Fig. 2 is that power-balance constraint of the invention adjusts process schematic.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
The present invention provides the sending end power grid Unit Combination progress control method that a kind of new energy participates in electric quantity balancing, including with
Lower step:
1) annual load curve, annual wind-powered electricity generation, photovoltaic power output probabilistic forecasting confidecne curve, fired power generating unit coal consumption system are obtained
Number, minimum open, the basic datas such as downtime, upper and lower climbing rate, and then obtains the prediction power output confidence interval and biography of new energy
It is flat to participate in systematic electricity electricity with new energy power output prediction confidence intervals lower limit for the booting prioritised list of system generating set
Weighing apparatus;
2) it introduces spare unit pond and abandons motor group pond, while considering that new energy participates in balance, spinning reserve constraint and most
Small start-off time constraints control set state, optimization system balance of electric power and ener process;
3) set state obtained using step 2) is flat based on the power for considering unit climbing rate as the input of particle swarm algorithm
Weighing apparatus adjustment particle position, obtains the optimal output of particle swarm algorithm, controls Unit Combination operating status according to the optimal output.
New energy includes wind-powered electricity generation, solar energy etc., and the 10%-20% of new energy installed capacity is generally taken to contribute as new energy
Confidence capacity can take at night 0 for photovoltaic power generation to take 10% daytime.
Conventional electric power generation unit includes fired power generating unit and Hydropower Unit.It, can be according to fired power generating unit energy consumption for fired power generating unit
Index obtains booting prioritised list.When considering thermoelectricity booting tab sequential, the important support node fire of power grid security is ensured
The fired power generating unit priority of motor group highest priority, energy consumption bottom is high.
The specific power coal consumption of thermoelectricity generating set in different power output ranges is not identical, thus may cause unit
Priority changes with the change of power output range.Therefore, the present invention is reflection unit coal consumption situation under different power outputs,
It is as follows to introduce adjustable generating set coal consumption index.
[0,1] α ∈ in formula, referred to as power output Dynamic gene, according to the f under different α values1, i, available generating set
The coal consumption for power generation ordering scenario under different power outputs.The adjustment of generating set state is carried out accordingly, can be obtained closer to optimal solution
Unit Commitment plan.The value of α is adjusted, available different unit priority repeats the above steps, to obtain one group
Different initial solutions.
In addition, the maximum unit of start-up cost that should give priority in arranging for is switched in long duration and runs, is frequently opened, shut down with reduction
Increase total system start-up cost, therefore introduces second sequence reference factor, start-up cost reference factor:
Finally, the power output upper limit highest and multiple electricity of economy preferably unit should allow, make with load variations and the machine of start and stop
Group quantity is reduced, and is further reduced start-up cost, therefore introduces third sequence reference factor, the upper limit reference factor of contributing:
Three sequence reference factors of unit are smaller, more preferential starting.Notice f2, i> 1, f3, i> 1, f1, i< 1, directly
Three is added to be ranked up and may cause total sequence index by f2, i、f3, iIt is leading, it is improved using following sequence index:
F=index1, i+index2, i+index3, i
In formula, index1, i、index2, i、index3, iRespectively press adjustable generating set coal consumption index factor, start-up cost
The ranking of unit i after reference factor and the power output upper limit reference factor sort from small to large, f value is smaller, and corresponding unit more preferentially opens
It is dynamic.
Water power power up sequences table is formed according to the regulation performance of water power for Hydropower Unit.According to Optimized Operation, save
The principle of the energy before the water power of general without regulation comes most, is adjusted followed by day, season is adjusted, year is adjusted, many years tune
Section.For the power station with reservoir with regulating power, generating capacity is removed to be restricted by Hydropower Unit technical factor itself
Except, also by the artificial restraint of water management department, therefore, when considering that power output and climbing rate constrain, it should by this
Some factors are taken into account, and smaller section is taken.
As shown in Figure 1, above-mentioned Unit Combination progress control method can specifically describe are as follows:
Step S101: the new energy such as wind-powered electricity generation and solar energy prediction power output confidence interval curves are obtained.
Step S102: according to the regulation performance of water power, water power power up sequences table is formed.
Step S103: according to fired power generating unit energy consumption index, fired power generating unit booting list is formed.
Step S104: do you according to load prediction curve, judge that subsequent period load increment is positive? if so, turning next
Step, if not, going to step S112.
Step S105: under channel constraints, unit starting is selected from motor group pond is abandoned.It is logical for what is currently taken
Road, the abandoning motor group portion below channel participate in this starting operation.
Abandon motor group pond: record is currently at the unit id for abandoning electricity condition, and abandoning motor group mainly includes abandoning the water of water
Motor group and just abandonment, abandon light new energy unit.
Step S106: after abandoning motor group pond unit starting, if meet load growth and revolve standby capacity requirement? if it is not, turning
In next step, if so, going to step S118.
Step S107: under channel constraints, start clean energy resource unit.
Step S108: after judging clean energy resource unit starting, if meet load growth and revolve standby capacity requirement? if it is not,
Turn in next step, if so, going to step S118.
Step S109: from spare unit pond, selecting fired power generating unit starting, is wanted with meeting load growth and revolving standby capacity
It asks.
Spare unit pond: record is currently at the fired power generating unit id of hot stand-by duty, the second level as spinning reserve unit
It is spare, according to spinning reserve capacity demand and load growth situation, the Dynamic Maintenance unit pond, safeguards system safety and stability economy
Operation.
Step S110: do you judge to meet load growth demand after the fired power generating unit in spare unit pond all starts? if it is not,
Turn in next step, if so, going to step S118.
Step S111: it is current it is all start unit starting after, be unable to satisfy load growth needs, then carry out limited load
Control, and start fired power generating unit, increase the unit quantity in spare unit pond.
Step S112: after insignificant support fired power generating unit is shut down in judgement, in 24 hours, if meet system rotation it is standby constrain and
Is load reduced? if so, shutting down qualified unit, S113 is gone to step, if it is not, directly executing step S113.
Step S113: after many years regulation Hydropower Unit is shut down in judgement, in 3 hours, if meet the standby constraint of system rotation and bear
Is lotus reduced? if so, shutting down qualified unit, S114 is gone to step, if it is not, directly executing step S114.
Step S114: after year adjusting Hydropower Unit is shut down in judgement, in 3 hours, if meet the standby constraint of system rotation and load
Reduce? if so, shutting down qualified unit, S115 is gone to step, if it is not, directly executing step S115.
Step S115: after season adjusting Hydropower Unit is shut down in judgement, in 3 hours, if meet the standby constraint of system rotation and load
Reduce? if so, shutting down qualified unit, S116 is gone to step, if it is not, directly executing step S116.
Step S116: after new energy unit is shut down in judgement, if meet the standby constraint of system rotation and load is reduced? if so, closing
Stop qualified unit, go to step S117, if it is not, directly executing step S117.
Step S117: after day adjusting Hydropower Unit is shut down in judgement, if meet the standby constraint of system rotation and load is reduced? if
It is to shut down qualified unit, goes to step S118, if it is not, directly executes step S118.
Step S118: do you judge that all periods arrange to finish? if so, turning in next step, if it is not, going to step 2.
Step S119: it is input that Unit Commitment, which is arranged as Optimized Operation particle swarm algorithm,.
Step S120: initialization the number of iterations iter=1 creates initial population, and adjust particle position, it is made to meet function
Rate Constraints of Equilibrium.
Step S121: particle rapidity and position are updated.
Does step S122:iter=iter+1 judge iter < itermax? if so, turning to terminate, if it is not, turning previous step.
In above-mentioned steps S109, when selecting fired power generating unit starting from spare unit pond, according to minimum startup-shutdown time tune
Whole initial Unit Commitment plan.State according to three classes constraint adjustment generating set: the original state constraint of generating set, power generation
The power constraint and unit minimum of machine open/downtime limitation.
It is as follows that generating set state adjusts process:
1. the value of α is arranged, prioritised list is formed.
2. load increases in period t, determine that needing successively to open how many units is just able to satisfy according to priority list
Workload demand adds spinning reserve.
3. calculating since period t, " the necessary available machine time " of unit i, M_ON is usediIt indicates.M_ONiThat is load is from period t
It is reduced to afterwards and does not need unit i boot system and be still able to satisfy spinning reserve to constrain passed through when number of segment.If M_ONi< TOi, then
Continue that unit i booting is kept to meet minimum available machine time constraint until it, conversely, then entering in next step.
4. load reduces in period t, judgement is shut down on preferred list after unit i, and it is standby whether system is able to satisfy rotation
With constraint.If it is not, unit i booting is kept, conversely, into 5. judging whether to shut down unit i.
5. calculating since period t, " necessity shuts down the time " of unit i uses M_OFFiIt indicates.M_OFFiI.e. load from when
Increase to after section t to needing unit i boot system to be just able to satisfy the passed through when number of segment of spinning reserve constraint.If M_OFFi<
TSi, then still need to that unit i is kept to open, because as load increases, if being not turned on unit i spinning reserve will be unable to meet.If M_
OFFi≥TSi, and unit i has reached the minimum available machine time, then shuts down unit i, into subsequent period.
6. detection unit first initially shuts down whether the time is less than its minimum in unit original state constraint adjustment member
Shut down the time, if so, and the unit need to be switched on (2. calculating through step), then keep the unit to shut down, detection is preferential suitable
Next unit on sequence table.If this unit be initially powered up the time less than its minimum available machine time or its initially shut down the time and be greater than
Its minimum downtime, then open the unit.This step is repeated to all units until the whole detection of its original state finishes.
7. adjusting the value of α, available different unit priority repeats the above steps, to obtain one group of difference
Initial solution.
The objective function that the particle swarm algorithm uses are as follows:
Wherein, number of segment when T is total, Nh、Ns、NxIt is thermoelectricity, water power, new energy unit number, P respectivelyHi, tIt is fired power generating unit i
In the power output of period t, PSI, tIt is power output of the Hydropower Unit i in period t, PXI, tIt is power output of the new energy unit i in period t, uI, t
It is start and stop state of the unit i in period t, is indicated with 0 or 1, FHI, t(PHI, t) it is operating cost of the fired power generating unit i in period t,
STiIt is the start-up and shut-down costs of fired power generating unit i, FSI, t(PSI, t) it is operating cost of the Hydropower Unit i in period t, FXI, t(PXI, t) be
Operating cost of the new energy unit i in period t.
A in formulai、bi、ciFor the coal consumption coefficient of unit i.SHi、SCiIt is thermal starting, the cold start-up expense of unit i respectively.TSi
The minimum downtime of unit i, TI, offIt is the time of unit i persistently shut down, TCiIt is the cold start-up time of unit i.
The constraint condition of use includes system power Constraints of Equilibrium, the constraint of unit output bound, spinning reserve constraint, machine
Group Climing constant and startup and shutdown of units time-constrain.
System power Constraints of Equilibrium:
In formula, PHI, tIt is power output of the fired power generating unit i in period t, PSI, tIt is power output of the Hydropower Unit i in period t, PXI, tIt is
Power output of the new energy unit i in period t, uI, tIt is start and stop state of the unit i in period t, is indicated with 0 or 1, DtIt is the negative of period t
Lotus demand.
The constraint of unit output bound:
PHI, min≤PHI, t≤PHI, max
PSI, min≤PSI, t≤PSI, max
PXI, min≤PXI, t≤PXI, max
PH in formulaI, max、PHI, minIt is fired power generating unit i minimax technology power output, PSI, max、PSI, minIt is Hydropower Unit technology
Power output bound, PXI, max、PXI, minIt is new energy unit output bound.
Spinning reserve constraint
In formula, uI, tIt is start and stop state of the unit i in period t, is indicated with 0 or 1, is power output of the fired power generating unit i in period t,
PSI, tIt is power output of the Hydropower Unit i in period t, PXT, minIt is new energy in period t prediction power output lower limit of confidence interval, generally may be used
Take 90% confidence interval, Nh、NsIt is thermoelectricity, water power group number, P respectivelytIt is total load of the system in the t period, RtSystem when
Spinning reserve needed for section t.
Unit ramp loss
-PHI, down≤PHI, t-PHI, t-1≤PHI, up
-PSI, down≤PSI, t-PSI, t-1≤PSI, up
-PXI, down≤PXI, t-PXI, t-1≤PXI, up
In formula, PHI, down、PHI, upIt is the upper and lower climbing rate of fired power generating unit, PSI, down、PSI, upIt is the upper and lower climbing of Hydropower Unit
Rate, PXI, down、PXI, upIt is the upper and lower climbing rate of new energy unit, PHI, t、PSI, t、PXI, tIt is respectively i-th thermoelectricity, water power, new
Power output of the energy unit in the t period.
Startup and shutdown of units time-constrain
TI, on≥TOi
TI, off≥TSi
T in formulaI, onIt is open state duration, the TO of unit iiIt is the minimum available machine time of unit i.TSiUnit i's
Minimum downtime, TI, offIt is the time of unit i persistently shut down.
The power-balance constraint of system is an equality constraint, and using the more difficult satisfaction of original PSO, the present invention proposes consideration machine
The power-balance adjustable strategies of group climbing rate, guarantee the multifarious of particle while avoiding rapidly converging to locally optimal solution.For
This, the present invention introduces the array of two constraint unit output ranges: PH (i) and PL (i), PH (i) are for highest power output, PL (i)
Minimum output.
PH (i) and PL (i) are obtained in the following manner:
To each unit i in each period t, if uit=1, ui(t-1)=0, then PH (i)=min { PI, max, RUi, PL (i)
=max { PI, min, RDi};
If uit=1, ui(t-1)=1, then PH (i)=min { PI, m α x, PI, t-1+RUi, PL (i)=max { PI, min, PI, t-1-
RDi};
If t=1, PH (i)=PI, max, PL (i)=PI, min;
If uit=1, ui(t+1)=0, then PH (i)=PL (i)=PI, min;
If uit=1, ui(t+1)=1, then PH (i)=min { PH (i), PI, t+1-RDi, PL (i)=max { PL (i), PI, t+1-
RUi}。
It is specific as shown in Figure 2 based on the power-balance adjustment particle position for considering unit climbing rate, comprising:
Step S201, initialization period t=1;
All unit output summation SUM of step S202, calculation interval ttAnd the power output range PH (i) of each unit i and
PL (i),;
Step S203 establishes a booting list, which, which is stored with, is in the unit of open state in period t;
Step S204, judges whether there is SUMt< DtAnd count > 0, if so, S205 is thened follow the steps, if it is not, then holding
Row step S206, wherein DtIndicate that the workload demand of period t, count indicate unit number in booting list;
Step S205 chooses unit i from the booting list, enables PI, t=PH (i), recalculates SUMt, judge whether
There are SUMt< Dt, if so, thening follow the steps S206, if it is not, then deleting unit i from booting list, count is updated, is returned
Step S204;
Step S206 re-establishes booting list, updates count;
Step S207, judges whether there is SUMt> DtAnd count > 0, if so, S208 is thened follow the steps, if it is not, then holding
Row step S209;
Step S208 chooses unit i from the booting list, enables PI, t=PL (i), recalculates SUMt, judge whether
There are SUMt< Dt, if so, executing step S209, if it is not, then deleting unit i from booting list, update count;
Step S209, if SUM at this timet≠DtAnd count > 0, then enable PI, t=min { PI, t-(SUMt-Dt), PH (i) }, it is no
Then, PI, t=max { PI, t, PL (i) };
Step S210 judges whether there is t < T, if so, t=t+1, return step S202, if it is not, then terminating.
In particle swarm algorithm, particle position and speed update calculation formula are as follows:
W is inertia weight parameter in formula, for the ability of searching optimum of control algolithm, c1And c2It is accelerated factor.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811459376.3A CN109390981B (en) | 2018-11-30 | 2018-11-30 | Combined operation control method of sending-end power grid units with new energy participating in power balance |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811459376.3A CN109390981B (en) | 2018-11-30 | 2018-11-30 | Combined operation control method of sending-end power grid units with new energy participating in power balance |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109390981A true CN109390981A (en) | 2019-02-26 |
CN109390981B CN109390981B (en) | 2022-05-27 |
Family
ID=65430236
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201811459376.3A Active CN109390981B (en) | 2018-11-30 | 2018-11-30 | Combined operation control method of sending-end power grid units with new energy participating in power balance |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109390981B (en) |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110266058A (en) * | 2019-05-31 | 2019-09-20 | 国网山东省电力公司济南供电公司 | A Modeling and Solution Method of Unit Combination Model Based on Interval Optimization |
CN110826773A (en) * | 2019-10-17 | 2020-02-21 | 内蒙古电力(集团)有限责任公司电力调度控制分公司 | A method for optimizing the monthly power generation plan of thermal power units considering new energy access |
CN112186734A (en) * | 2020-08-20 | 2021-01-05 | 西安交通大学 | Medium-and-long-term operation simulation method for power system, storage medium and computing equipment |
CN114336777A (en) * | 2021-11-29 | 2022-04-12 | 中国华能集团清洁能源技术研究院有限公司 | Thermal power generating unit starting sequence determination method and system considering energy utilization sequence |
CN115307274A (en) * | 2022-10-12 | 2022-11-08 | 蘑菇物联技术(深圳)有限公司 | Method, apparatus and storage medium for controlling host of air conditioning system |
CN116488212A (en) * | 2023-06-19 | 2023-07-25 | 长沙电机厂集团长瑞有限公司 | Method and system for virtually controlling multiple motor groups to perform power energy storage configuration |
CN116760111A (en) * | 2023-08-23 | 2023-09-15 | 太原理工大学 | Distributed energy access and electric energy storage control method, device, equipment and medium |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120310608A1 (en) * | 2011-06-03 | 2012-12-06 | Nikovski Daniel N | Method for Scheduling Power Generators Based on Optimal Configurations and Approximate Dynamic Programming |
CN103345663A (en) * | 2013-07-18 | 2013-10-09 | 厦门大学 | Combinatorial optimization method of electric power system set considering creep speed constraints |
CN104978629A (en) * | 2015-06-18 | 2015-10-14 | 广西电网有限责任公司 | Optimal multi-type power supply complementary peak-adjusting mode and model thereof |
CN104993524A (en) * | 2015-07-17 | 2015-10-21 | 三峡大学 | Wind power-containing electric system dynamic dispatching method based on improved discrete particle swarm optimization |
CN105760959A (en) * | 2016-02-24 | 2016-07-13 | 武汉大学 | Unit commitment (UC) optimization method based on two-phase firefly encoding |
CN108075494A (en) * | 2016-11-10 | 2018-05-25 | 中国电力科学研究院 | A kind of Unit Combination method a few days ago taken into account new energy consumption and performed with transaction |
-
2018
- 2018-11-30 CN CN201811459376.3A patent/CN109390981B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120310608A1 (en) * | 2011-06-03 | 2012-12-06 | Nikovski Daniel N | Method for Scheduling Power Generators Based on Optimal Configurations and Approximate Dynamic Programming |
CN103345663A (en) * | 2013-07-18 | 2013-10-09 | 厦门大学 | Combinatorial optimization method of electric power system set considering creep speed constraints |
CN104978629A (en) * | 2015-06-18 | 2015-10-14 | 广西电网有限责任公司 | Optimal multi-type power supply complementary peak-adjusting mode and model thereof |
CN104993524A (en) * | 2015-07-17 | 2015-10-21 | 三峡大学 | Wind power-containing electric system dynamic dispatching method based on improved discrete particle swarm optimization |
CN105760959A (en) * | 2016-02-24 | 2016-07-13 | 武汉大学 | Unit commitment (UC) optimization method based on two-phase firefly encoding |
CN108075494A (en) * | 2016-11-10 | 2018-05-25 | 中国电力科学研究院 | A kind of Unit Combination method a few days ago taken into account new energy consumption and performed with transaction |
Non-Patent Citations (4)
Title |
---|
ABDULLAH M. ELSAYED等: "A new priority list unit commitment method for large-scale power systems", 《2017 NINETEENTH INTERNATIONAL MIDDLE EAST POWER SYSTEMS CONFERENCE (MEPCON)》 * |
MD. SAJID ALAM等: "Priority list and particle swarm optimization based unit commitment of thermal units including renewable uncertainties", 《2016 IEEE INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON)》 * |
李整: "基于粒子群优化算法的机组组合问题的研究", 《中国优秀博硕士学位论文全文数据库(博士)基础科学辑》 * |
黎静华等: "适合于机组组合问题的扩展优先顺序法", 《电力系统保护与控制》 * |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110266058A (en) * | 2019-05-31 | 2019-09-20 | 国网山东省电力公司济南供电公司 | A Modeling and Solution Method of Unit Combination Model Based on Interval Optimization |
CN110266058B (en) * | 2019-05-31 | 2023-07-14 | 国网山东省电力公司济南供电公司 | A Modeling and Solution Method of Unit Combination Model Based on Interval Optimization |
CN110826773A (en) * | 2019-10-17 | 2020-02-21 | 内蒙古电力(集团)有限责任公司电力调度控制分公司 | A method for optimizing the monthly power generation plan of thermal power units considering new energy access |
CN110826773B (en) * | 2019-10-17 | 2023-04-07 | 内蒙古电力(集团)有限责任公司电力调度控制分公司 | Thermal power generating unit monthly power generation plan optimization method considering new energy access |
CN112186734A (en) * | 2020-08-20 | 2021-01-05 | 西安交通大学 | Medium-and-long-term operation simulation method for power system, storage medium and computing equipment |
CN114336777A (en) * | 2021-11-29 | 2022-04-12 | 中国华能集团清洁能源技术研究院有限公司 | Thermal power generating unit starting sequence determination method and system considering energy utilization sequence |
CN114336777B (en) * | 2021-11-29 | 2023-08-25 | 中国华能集团清洁能源技术研究院有限公司 | Method and system for determining start-up sequence of thermal power units considering energy utilization sequence |
CN115307274A (en) * | 2022-10-12 | 2022-11-08 | 蘑菇物联技术(深圳)有限公司 | Method, apparatus and storage medium for controlling host of air conditioning system |
CN116488212A (en) * | 2023-06-19 | 2023-07-25 | 长沙电机厂集团长瑞有限公司 | Method and system for virtually controlling multiple motor groups to perform power energy storage configuration |
CN116488212B (en) * | 2023-06-19 | 2023-08-22 | 长沙电机厂集团长瑞有限公司 | Method and system for virtually controlling multiple motor groups to perform power energy storage configuration |
CN116760111A (en) * | 2023-08-23 | 2023-09-15 | 太原理工大学 | Distributed energy access and electric energy storage control method, device, equipment and medium |
CN116760111B (en) * | 2023-08-23 | 2023-11-10 | 太原理工大学 | Distributed energy access and electric energy storage control method, device, equipment and medium |
Also Published As
Publication number | Publication date |
---|---|
CN109390981B (en) | 2022-05-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109390981A (en) | The sending end power grid Unit Combination progress control method of new energy participation electric quantity balancing | |
AU2020100983A4 (en) | Multi-energy complementary system two-stage optimization scheduling method and system considering source-storage-load cooperation | |
EP2821724B1 (en) | Optimization apparatus, optimization method, and optimization program for storing electricity and heat. | |
CN111555281B (en) | A simulation method and device for flexible resource allocation of power system | |
Lu et al. | Short-term scheduling of battery in a grid-connected PV/battery system | |
US9568901B2 (en) | Multi-objective energy management methods for micro-grids | |
CN111008739B (en) | A method and system for optimal regulation and income distribution of a cogeneration virtual power plant | |
Tutkun | Minimization of operational cost for an off-grid renewable hybrid system to generate electricity in residential buildings through the SVM and the BCGA methods | |
CN115566703A (en) | Distributed photovoltaic and electricity-hydrogen hybrid energy storage planning method oriented to multi-energy complementation | |
CN111682536A (en) | A Stochastic-Robust Optimal Operation Method for Virtual Power Plants Participating in Day-ahead Dual Markets | |
CN109546689B (en) | Two-stage unit combined operation control method suitable for large-scale system | |
CN109412158A (en) | A kind of sending end power grid Unit Combination progress control method for considering to abandon energy cost constraint | |
CN112952847A (en) | Multi-region active power distribution system peak regulation optimization method considering electricity demand elasticity | |
CN116307094A (en) | Urban water supply optimal scheduling method based on multi-target particle swarm algorithm | |
Yang et al. | A GUI-based simulation platform for energy and comfort management in Zero-Energy Buildings | |
Zhu et al. | Environmental and economic scheduling for wind-pumped storage-thermal integrated energy system based on priority ranking | |
CN119168339A (en) | Energy interconnection intelligent management control method, device, computer equipment and medium | |
CN106229992A (en) | Energy management method for micro-grid under electricity market | |
CN115976555B (en) | A composite water electrolysis hydrogen production system | |
CN117744894A (en) | An active learning agent optimization method for integrated energy systems | |
CN115293595B (en) | A virtual power plant aggregation capacity assessment method considering photovoltaic output uncertainty | |
Setz et al. | Energy smart buildings: Parallel uniform cost-search with energy storage and generation | |
Xi et al. | Unit commitment problems in power systems with wind farms based on probability offset binary particle swarm optimization algorithm | |
Du et al. | Temporal Rolling-based Coordinated Operation of A Multi-Energy Microgrid Considering Thermal Inertia | |
Wenfei et al. | Optimal Operation Strategy of Virtual Power Plant with Building Inverter Air Conditioner |
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 | ||
GR01 | Patent grant | ||
GR01 | Patent grant |