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 PDF

Info

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
Application number
CN201811459376.3A
Other languages
Chinese (zh)
Other versions
CN109390981B (en
Inventor
周全
路亮
魏明奎
江栗
蔡绍荣
柳璐
陈佩华
程浩忠
袁杨
张程铭
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.)
State Grid Corp of China SGCC
Southwest Branch of State Grid Corp
Shanghai Jiao Tong University
Original Assignee
State Grid Corp of China SGCC
Southwest Branch of State Grid Corp
Shanghai Jiao Tong University
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 State Grid Corp of China SGCC, Southwest Branch of State Grid Corp, Shanghai Jiao Tong University filed Critical State Grid Corp of China SGCC
Priority to CN201811459376.3A priority Critical patent/CN109390981B/en
Publication of CN109390981A publication Critical patent/CN109390981A/en
Application granted granted Critical
Publication of CN109390981B publication Critical patent/CN109390981B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46Controlling 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

The sending end power grid Unit Combination progress control method of new energy participation electric quantity balancing
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)

1.一种新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,包括以下步骤:1. a method for controlling the combined operation of sending end power grid units that new energy sources participate in power balance, is characterized in that, comprises the following steps: 1)获取新能源的预测出力置信区间和传统发电机组的开机优先顺序列表;1) Obtain the confidence interval of the predicted output of the new energy source and the power-on priority list of the traditional generator set; 2)引入备用机组池和弃电机组池,同时考虑新能源参与平衡、旋转备用约束和最小启停时间约束,控制机组状态;2) Introduce a pool of standby units and a pool of abandoned generators, and at the same time consider the balance of new energy participation, spinning reserve constraints and minimum start-stop time constraints to control the state of the units; 3)以步骤2)获得的机组状态为粒子群算法的输入,基于考虑机组爬坡率的功率平衡调整粒子位置,获得粒子群算法的最优输出,根据所述最优输出控制机组组合运行状态。3) Take the unit state obtained in step 2) as the input of the particle swarm algorithm, adjust the particle position based on the power balance considering the unit ramp rate, obtain the optimal output of the particle swarm algorithm, and control the combined operation state of the unit according to the optimal output . 2.根据权利要求1所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述传统发电机组包括火电机组和水电机组,基于排序参考因子获得火电机组的开机优先顺序列表,所述排序参考因子包括可调发电机组煤耗指标因子、启动成本参考因子和出力上限参考因子,根据排序参考因子的参数选择的不同,获得不同的火电机组开机优先顺序列表;2. the sending end power grid unit combination operation control method that new energy sources participate in power balance according to claim 1, it is characterized in that, described traditional generating unit comprises thermal power unit and hydroelectric unit, obtains the startup priority of thermal power unit based on sorting reference factor a sequence list, where the ranking reference factor includes an index factor for coal consumption of an adjustable generator set, a reference factor for startup cost, and a reference factor for an output upper limit, and different thermal power unit start-up priority lists are obtained according to different parameter selections of the ranking reference factor; 所述水电机组根据调节性能获得对应的开机优先顺序列表。The hydroelectric unit obtains a corresponding start-up priority list according to the adjustment performance. 3.根据权利要求2所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,各排序参考因子通过以下公式组合后确定发电机组开机优先顺序:3. The method for controlling the combined operation of sending-end power grid units in which new energy sources participate in power balance according to claim 2, wherein each ranking reference factor is combined by the following formula to determine the power-on priority of the generating units: f=index1,i+index2,i+index3,i f = index 1, i + index 2, i + index 3, i 式中,index1,i、index2,i、index3,i分别为按可调发电机组煤耗指标因子、启动成本参考因子和出力上限参考因子从小到大排序后机组i的排名,f值越小,对应机组越优先启动。In the formula, index 1, i , index 2, i , and index 3, i are respectively the ranking of the unit i after sorting from small to large according to the coal consumption index factor of the adjustable generator set, the starting cost reference factor and the output upper limit reference factor. Smaller, the higher priority the corresponding unit is to start. 4.根据权利要求1所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述弃电机组池包括正在弃水的水电机组及正在弃风、弃光的新能源机组;4. The method for controlling the combined operation of sending-end power grid units in which new energy sources participate in electricity balance according to claim 1, wherein the pool of abandoned power units includes hydroelectric units that are abandoning water and new power units that are abandoning wind and light. energy unit; 备用机组池包括当前处于热备用状态的火电机组。The standby unit pool includes thermal power units that are currently in a hot standby state. 5.根据权利要求1所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述步骤2)具体包括以下步骤:5. The method for controlling the combined operation of sending-end power grid units in which new energy sources participate in power balance according to claim 1, wherein the step 2) specifically comprises the following steps: a1)根据负荷预测曲线,判断下一时段负荷预测增量是否为正,若是,则执行步骤a2),若否,则执行步骤a3);a1) According to the load forecast curve, determine whether the load forecast increment in the next period is positive, if so, execute step a2), if not, execute step a3); a2)基于所述开机优先顺序列表,依次控制弃电机组池、新能源机组、备用机组池、火电机组中的相应机组启动,直至满足负载增长及旋转备用约束,执行步骤a4);a2) Based on the start-up priority list, control the corresponding units in the abandoned power generating unit pool, the new energy generating unit, the standby generating unit pool, and the thermal power generating unit to start up in turn, until the load growth and spinning reserve constraints are met, and step a4) is performed; a3)基于所述开机优先顺序列表,依次控制火电机组、多年调节水电机组、年调节水电机组、季调节水电机组、新能源机组、日调节水电机组中的相应机组关停,直至满足旋转备用约束及负载减少,执行步骤a4);a3) Based on the start-up priority list, control the thermal power unit, the multi-year adjustment hydroelectric unit, the annual adjustment hydroelectric unit, the seasonal adjustment hydroelectric unit, the new energy unit, and the daily adjustment hydroelectric unit to shut down the corresponding units in turn, until the rotating standby constraints are met and the load is reduced, perform step a4); a4)判断是否所有时段的机组状态均控制完毕,若是,则执行步骤3),若否,则返回步骤a1)。a4) Determine whether the unit states of all time periods have been controlled, if so, go to step 3), if not, go back to step a1). 6.根据权利要求5所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述步骤a2)具体包括:6. The method for controlling the combined operation of sending-end power grid units in which new energy sources participate in power balance according to claim 5, wherein the step a2) specifically comprises: 201)在通道约束条件下,从弃电机组池中选择机组启动;201) Under the condition of channel constraints, select a generator set from the abandoned generator set pool to start; 202)判断弃电机组池相应机组启动后是否满足负载增长及旋转备用约束,若是,则执行步骤a4),若否,则执行步骤203);202) Determine whether the corresponding unit of the abandoned power unit pool satisfies the load growth and rotation reserve constraints after starting up, if so, execute step a4), if not, execute step 203); 203)在通道约束条件下,根据所述预测出力置信区间启动新能源机组;203) Under the condition of channel constraints, start the new energy generating unit according to the predicted output confidence interval; 204)判断新能源机组启动后是否能满足负载增长及旋转备用约束,若是,则执行步骤a4),若否,则执行步骤205);204) Determine whether the new energy unit can meet the load growth and rotation reserve constraints after starting, if so, execute step a4), if not, execute step 205); 205)从备用机组池中选择火电机组启动;205) Select a thermal power unit to start from the standby unit pool; 206)判断火电机组启动后是否能满足负载增长及旋转备用约束,若是,则执行步骤a4),若否,则执行步骤207);206) judging whether the load growth and rotation reserve constraints can be met after the thermal power unit is started, if yes, then execute step a4), if not, then execute step 207); 207)进行限负荷控制,并启动火电机组,增加备用机组池中的机组数量,执行步骤a4)。207) Carry out load limit control, start the thermal power unit, increase the number of units in the standby unit pool, and execute step a4). 7.根据权利要求5所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述步骤a3)具体包括:7. The method for controlling the combined operation of sending-end power grid units in which new energy sources participate in power balance according to claim 5, wherein the step a3) specifically comprises: 301)判断关停非重要支撑火电机组后,24小时内是否满足旋转备用约束及负载减少,若是,则关停符合条件的机组后执行步骤302),若否,则执行步骤302);301) after judging the shutdown of the non-important support thermal power unit, whether to meet the rotation reserve constraint and load reduction within 24 hours, if so, then shut down the qualified unit and then execute step 302), if not, then execute step 302); 302)判断关停多年调节水电机组后,3小时内是否满足旋转备用约束及负载减少,若是,则关停符合条件的机组后执行步骤303),若否,则执行步骤303);302) After judging whether the rotating standby constraints and load reduction are satisfied within 3 hours after shutting down the multi-year regulating hydroelectric unit, if yes, then shutting down the qualified unit and then executing step 303), if not, then executing step 303); 303)判断关停年调节水电机组后,3小时内是否满足旋转备用约束及负载减少,若是,则关停符合条件的机组后执行步骤304),若否,则执行步骤304);303) After judging whether the annual regulation hydropower unit is shut down, whether the rotation reserve constraint and load reduction are satisfied within 3 hours, if yes, then shut down the qualified unit and then execute step 304), if not, execute step 304); 304)判断关停季调节水电机组后,3小时内是否满足旋转备用约束及负载减少,若是,则关停符合条件的机组后执行步骤305),若否,则执行步骤305);304) After judging the shutdown of the seasonally regulated hydroelectric unit, whether the rotation reserve constraint and load reduction are satisfied within 3 hours, if so, then shut down the qualified unit and then execute step 305), if not, execute step 305); 305)判断关停新能源机组后,是否满足负载减少,若是,则关停符合条件的机组后执行步骤306),若否,则执行步骤306);305) After judging whether the load reduction is satisfied after shutting down the new energy unit, if yes, then shutting down the qualified unit and then executing step 306), if not, executing step 306); 306)判断关停日调节水电机组后,是否满足负载减少,若是,则关停符合条件的机组后执行步骤a4),若否,则执行步骤a4)。306) Determine whether the load reduction is satisfied after the adjustment of the hydroelectric unit on the shutdown day, if yes, then shut down the qualified unit and then execute step a4), if not, execute step a4). 8.根据权利要求1所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述粒子群算法采用的目标函数为:8. The method for controlling the combined operation of sending-end power grid units in which new energy sources participate in power balance according to claim 1, wherein the objective function adopted by the particle swarm algorithm is: 其中,T是总时段数,Nh、Ns、Nx分别是火电、水电、新能源机组数,PHi,t是火电机组i在时段t的出力,PSi,t是水电机组i在时段t的出力,PXi,t是新能源机组i在时段t的出力,ui,t是机组i在时段t的启停状态,用0或1表示,FHi,t(PHi,t)是火电机组i在时段t的运行成本,STi是火电机组i的启停成本,FSi,t(PSi,t)是水电机组i在时段t的运行成本,FXi,t(PXi,t)是新能源机组i在时段t的运行成本;Among them, T is the total number of time periods, N h , N s , and N x are the number of thermal power, hydropower, and new energy units, respectively, PH i,t is the output of thermal power unit i in time period t, PS i,t is the hydropower unit i in Output of time period t, PX i, t is the output of new energy unit i in time period t, ui , t is the start and stop state of unit i in time period t, represented by 0 or 1, FH i, t (PH i, t ) is the operating cost of thermal power unit i in time period t, ST i is the start-stop cost of thermal power unit i, FS i, t (PS i, t ) is the operating cost of hydroelectric power unit i in time period t, FX i, t (PX i, t i, t ) is the operating cost of new energy unit i in time period t; 采用的约束条件包括系统功率平衡约束、机组出力上下限约束、旋转备用约束、机组爬坡约束和机组起停时间约束。The constraints used include system power balance constraints, upper and lower output limits of units, rotating reserve constraints, unit climbing constraints and unit start-stop time constraints. 9.根据权利要求1所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述基于考虑机组爬坡率的功率平衡调整粒子位置具体为:9. The transmission-end power grid unit combination operation control method in which new energy sources participate in power balance according to claim 1, wherein the power balance adjustment particle position based on considering the unit ramp rate is specifically: 221)初始化时段t=1;221) initialization period t=1; 222)计算时段t所有机组出力总和SUMt以及每个机组i的出力范围PH(i)和PL(i),PH(i)为最高出力,PL(i)为最低出力;222) Calculate the total output SUM t of all units in the period t and the output ranges PH(i) and PL(i) of each unit i, where PH(i) is the highest output, and PL(i) is the lowest output; 223)建立一开机列表,该开机列表存储有在时段t处于开机状态的机组;223) establish a power-on list, and the power-on list stores the units that are in the power-on state during time period t; 224)判断是否存在SUMt<Dt且count>0,若是,则执行步骤225),若否,则执行步骤226),其中,Dt表示时段t的负荷需求,count表示开机列表中机组数;224) Judging whether there is SUM t < D t and count > 0, if so, go to step 225), if not, go to step 226), where D t represents the load demand of time period t, and count represents the number of units in the power-on list ; 225)从所述开机列表中选取机组i,令Pi,t=PH(i),重新计算SUMt,判断是否存在SUMt<Dt,若是,则执行步骤226),若否,则从开机列表中删除该机组i,更新count,返回步骤224);225) Select unit i from the startup list, set P i,t =PH(i), recalculate SUM t , judge whether there is SUM t <D t , if yes, then execute step 226), if not, then from Delete the unit i in the boot list, update the count, and return to step 224); 226)重新建立开机列表,更新count;226) Re-establish the boot list, and update the count; 227)判断是否存在SUMt>Dt且count>0,若是,则执行步骤228),若否,则执行步骤229);227) Judge whether there is SUM t > D t and count > 0, if so, execute step 228), if not, execute step 229); 228)从所述开机列表中选取机组i,令Pi,t=PL(i),重新计算SUMt,判断是否存在SUMt<Dt,若是,执行步骤229),若否,则从开机列表中删除该机组i,更新count;228) Select unit i from the power-on list, set P i,t =PL(i), recalculate SUM t , determine whether there is SUM t <D t , if so, go to step 229), if not, start from power-on Delete the unit i from the list and update the count; 229)若此时SUMt≠Dt且count>0,则令Pi,t=min{Pi,t-(SUMt-Dt),PH(i)},否则,Pi,t=max{Pi,t,PL(i)};229) If SUM t ≠D t and count>0, then let P i,t =min{P i,t -(SUM t -D t ),PH(i)}, otherwise, P i,t = max{P i, t , PL(i)}; 230)重复步骤222)-229),直至t=T。230) Repeat steps 222)-229) until t=T. 10.根据权利要求9所述的新能源参与电量平衡的送端电网机组组合运行控制方法,其特征在于,所述出力范围PH(i)和PL(i)通过以下方式获得:10. The method for controlling the combined operation of sending-end power grid units in which new energy sources participate in power balance according to claim 9, wherein the output ranges PH(i) and PL(i) are obtained in the following manner: 对每个机组i在每个时段t,若uit=1,ui(t-1)=0,则PH(i)=min{Pi,max,RUi},PL(i)=max{Pi,min,RDi};For each unit i in each time period t, if u it =1, u i(t-1) =0, then PH(i)=min{P i,max ,RU i }, PL(i)=max {P i, min , RD i }; 若uit=1,ui(t-1)=1,则PH(i)=min{Pi,max,Pi,t-1+RUi},PL(i)=max{Pi,min,Pi,t-1-RDi};If u it =1, u i(t-1 )=1, then PH(i)=min{P i, max , P i, t-1 +RU i }, PL(i)=max{P i, min , P i, t-1 -RD i }; 若t=1,则PH(i)=Pi,max,PL(i)=Pi,minIf t=1, then PH(i)=P i,max , PL(i)=P i,min ; 若uit=1,ui(t+1)=0,则PH(i)=PL(i)=Pi,minIf u it =1, u i(t+1) =0, then PH(i)=PL(i)=P i,min ; 若uit=1,ui(t+1)=1,则PH(i)=min{PH(i),Pi,t+1-RDi},PL(i)=max{PL(i),Pi,t+1-RUi};If u it =1, u i(t+1) =1, then PH(i)=min{PH(i), P i, t+1 -RD i }, PL(i)=max{PL(i ), P i, t+1 -RU i }; 其中,RDi、RUi表示机组下、上爬坡速率。Among them, RD i and RU i represent the descending and ascending ramp rates of the unit.
CN201811459376.3A 2018-11-30 2018-11-30 Combined operation control method of sending-end power grid units with new energy participating in power balance Active CN109390981B (en)

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)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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

Patent Citations (6)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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