CN105868841B - A kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation - Google Patents
A kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation Download PDFInfo
- Publication number
- CN105868841B CN105868841B CN201610161774.1A CN201610161774A CN105868841B CN 105868841 B CN105868841 B CN 105868841B CN 201610161774 A CN201610161774 A CN 201610161774A CN 105868841 B CN105868841 B CN 105868841B
- Authority
- CN
- China
- Prior art keywords
- power
- power plant
- particle
- wind
- electricity generation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000005611 electricity Effects 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 16
- 239000002245 particle Substances 0.000 claims abstract description 78
- 230000000694 effects Effects 0.000 claims abstract description 40
- 239000003245 coal Substances 0.000 claims description 13
- 230000005619 thermoelectricity Effects 0.000 claims description 11
- 230000009194 climbing Effects 0.000 claims description 7
- 230000009916 joint effect Effects 0.000 claims 2
- 241000196324 Embryophyta Species 0.000 claims 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims 1
- 240000002853 Nelumbo nucifera Species 0.000 claims 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 206010008190 Cerebrovascular accident Diseases 0.000 description 3
- 208000006011 Stroke Diseases 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention belongs to system for distribution network of power management and running and technical field of power grid management, and in particular to a kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation, firstly, by the one day acquisition wind-powered electricity generation prediction power in control centre and load prediction power；Secondly, wet season and dry season is divided to construct objective function and constraint function；Then PSO Algorithm objective function is used；Finally, going out activity of force between hydroelectric power plant and thermal power plant according to optimal de-assign；The present invention has fully considered the characteristic that the fluctuation of wind-powered electricity generation online is big and cost is small, it is divided to two periods of dry season and wet season to carry out analysis joint scheduling, and global optimizing is carried out to dispatching algorithm using particle swarm algorithm, obtain optimal solution carry out power distribution scheduling, realize renewable energy make full use of and maximization of economic benefit.
Description
Technical field
The invention belongs to system for distribution network of power management and running and technical field of power grid management, and in particular to one kind is based on wind
The geomantic omen fire combined scheduling method that electricity is preferentially surfed the Internet.
Background technique
Continuous with energy demand increases and petering out for fossil fuel resource and increasingly dashing forward for environmental problem
Out, wind-power electricity generation is as most one of the generation mode of economic development prospect in renewable energy, increasingly by the blueness of people
It looks at and payes attention to.Different from conventional electric power generation, wind power output power has intermittent and randomness, brings to the scheduling of electric system
New challenge.After wind-powered electricity generation online, electric system apoplexy extreme misery joint optimal operation is remarkable in economical benefits but calculates more complicated
Project.Traditional Hydro-Thermal Systems combined dispatching only accounts for the problem of thermal power plant's coal consumption characteristic minimum, does not consider different seasons
The fluctuation of water-saving electricity power output difference and wind-powered electricity generation.
Particle swarm algorithm is the new evolution algorithm of one kind developed in recent years, is to pass through iteration from RANDOM SOLUTION
Optimal solution is found, it is also the quality of solution to be evaluated by fitness, but it is more simpler than genetic algorithm rule, it does not lose
" fork " (Crossover) and " variation " (Mutation) of propagation algorithm is operated, it by follow the optimal value that current search arrives come
Find global optimum.It is general need to only list objective function and constraint function can carry out it is optimal required for iteration of simulations obtains
Solution, particle swarm algorithm of the present invention is universal code.
One Chinese patent, application number: CN201110111454.2, title are as follows: a kind of based on dispatching of power netwoks side demand
Geomantic omen fire combined scheduling method proposes a kind of geomantic omen fire combined scheduling method comprising following steps: step 1: from dispatching of power netwoks
It automates and obtains master data, prediction and planning data, historical data in the database of main station system；Step 2: construction geomantic omen fire
Integrated distribution model, objective function and constraint condition including model: step 3: by the master data obtained in step 1, prediction
It is input to planning data, historical data in the geomantic omen fire integrated distribution model constructed in step 2, using simplex method, interior point
The optimization algorithms such as method are solved；Step 4: the solving result that step 3 is obtained is calculated by the man-machine interface in main station system
Machine is provided to dispatcher, and dispatcher obtains traffic order with reference to above-mentioned solving result, is controlled and is dispatched by man-machine interface computer
Order issues, and plant stand end executes the traffic order assigned.The system relatively accurately can carry out power distribution to power plant, still
On the one hand the fluctuation and hydroelectric seasonality of wind power are not fully considered, one side Optimization Solution algorithm is excessively simple
It is single, easily fall into local optimum.Therefore, electric power system dispatching needs to establish multi-source and mutually helps the coordination system, to improve wind energy utilization,
It realizes distributing rationally for clean energy resource, reduces coal consumption and hydroelectric power plant abandons water, prevent flood season.
Summary of the invention
It is an object of the present invention to solving the above problem of the prior art, the present invention provides a kind of preferentially to be surfed the Internet based on wind-powered electricity generation
Geomantic omen fire combined scheduling method, the present invention has fully considered the characteristic that the fluctuation of wind-powered electricity generation online is big and cost is small, to dry season
With two period analysis geomantic omen fire combined dispatching strategies of wet season, realize power distribution scheduling to improve wind energy utilization, in order to
Realize above-mentioned purpose, The technical solution adopted by the invention is as follows:
A kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation, it is characterised in that: the following steps are included:
Step S1: by obtaining wind-powered electricity generation prediction power and load prediction power in scheduling；
Step S2: wind-power electricity generation is preferably all surfed the Internet, and is constructed respectively to the load prediction power in wet season and dry season
Objective function and constraint function, then distribution, which carries out combined dispatching, to be realized to wet season and dry season power；In dry season, by wind-force
The preferred all online of power generation, it is basic load that the efficient thermal power plant of fetching portion, which completely breaks out, first, then remaining load is distributed to
Hydroelectric power plant and thermal power plant carry out peak-frequency regulation and realize combined dispatching；Remaining thermal power plant and hydroelectric power plant are carried out together peak-frequency regulation with
Thermal power plant's processing fluctuation is reduced, thermal power plant's load pressure is reduced.In the wet season, wind-power electricity generation is preferably all surfed the Internet, to water power
The activity of force that goes out of factory and thermal power plant is allocated realization combined dispatching.Wind-powered electricity generation is preferentially all surfed the Internet, since wet season water is more, and
Consider flood season reason, takes hydroelectric power plant completely to send out strategy, remaining load is allocated in thermoelectricity inter-plant.
Step S3: PSO Algorithm objective function is used, seeks optimal solution and tune is allocated to load prediction power
Degree；
Step S4: activity of force is gone out between hydroelectric power plant and thermal power plant according to optimal de-assign.
As further scheme of the invention, the dry season realizes that combined dispatching includes the following steps:
Step S21: one day wind-powered electricity generation prediction power and load prediction power are obtained from control centre；
Based on step S22: wind-power electricity generation whole power is all surfed the Internet, and the efficient thermal power plant of fetching portion completely breaks out
Load, then remaining load is distributed into hydroelectric power plant and thermal power plant's progress peak-frequency regulation realization combined dispatching；Step S23: with thermoelectricity
Factory and hydroelectric power plant's power swing minimum building objective function are as follows:
Δ c=Δ Pg+ Δ Ps；
Wherein: Δ c is that hydroelectric power plant and thermal power plant's power always fluctuate, unit MW；Δ Pg is thermoelectricity field power swing and unit
MW；Δ Ps be hydroelectric power plant's power swing and, Pg is that thermal power plant goes out activity of force, and Ps is that hydroelectric power plant goes out activity of force, and r is machine group number, when i
Segment number；
Step S24: the activity of force bound out and climbing power of single thermal power plant are limited, and to meet geomantic omen fire
Combine that activity of force summation out is equal with load, the power output upper limit of the power of taken power supply requires 1.05 times that are greater than load peak, tool
Body limitation is as follows:
Pgrmin≤Pgri≤Pgrmax；
Piload=Pgi+Psi+Pwi；
Pgmax+Psmax+Pwmax≥1.05Ploadmax；
Wherein: PgrminFor power plant units power output power minimum, unit MW；PgrmaxGo out activity of force for power plant units
Maximum value, unit MW；PwmaxFor maximum wind prediction power, PloadmaxFor maximum wind load prediction power, PiloadFor some time
The lower load power of section；PgiGo out activity of force, Ps for thermal power plant under certain periodiPower plant, which is lauched, for certain period goes out activity of force, PwiFor some time
The lower wind-powered electricity generation prediction power of section；PgmaxFor thermal power plant's power output upper limit of the power, PsmaxFor hydroelectric power plant's power output upper limit of the power.
Step S25: optimizing is carried out by particle swarm algorithm, obtains meeting the optimal power solution under constraint condition, according to most
Excellent power results carry out power distribution to hydroelectric power plant and thermal power plant.
As further scheme of the invention, steps are as follows for the step S25 particle swarm algorithm optimizing:
Step S31: being generated the population of 20000 particles at random, positioned by the way of coding to each particle,
In, particle represents the performance number of different single thermal power plant or hydroelectric power plant；
Step S32: different speed and position are obtained according to each particle, is sought most by the constraint condition of step S24
Then excellent power solution eliminates the particle for being unsatisfactory for constraint condition；
Step S33: according to seeking the particle rapidity after optimal power solution and position, calculating target function, and according to target
Function size updates population, eliminates the particle for keeping objective function excessive, and generate class according to the speed and position that retain particle
As new particle group；
Step S34: repeating the continuous grey iterative generation new particle group of step S33, until the value of objective function tends towards stability, obtains
This generation population is decoded by minimal solution, that is, optimal solution of objective function, obtains the power distribution solution at each moment.
As further scheme of the invention, the wet season realizes that combined dispatching includes the following steps:
Step S41: one day wind-powered electricity generation prediction power, load prediction power and hydroelectric power plant Man Fagong are obtained from control centre
Rate；
Step S42: wind-power electricity generation whole power is all surfed the Internet, and is allocated to the activity of force that goes out of hydroelectric power plant and thermal power plant
Realize combined dispatching；
Step S43: it is as follows that objective function is constructed at least with thermal power plant's coal consumption:
Wherein, minc is the total consumption of coal of thermal power plant's power, unit t/h；ar,br,crFor thermoelectricity field coal consumption characteristic coefficient；PgriFor
Thermal power plant's generated output, unit MW；R is machine group number；Segment number when i；
Step S44: the activity of force bound out and climbing power of single thermal power plant are limited, and to meet geomantic omen fire
Combine that activity of force summation out is equal with load, the power output upper limit of the power of taken power supply requires 1.05 times that are greater than load peak, tool
Body limitation is as follows:
Pgrmin≤Pgri≤Pgrmax；
Piload=Pgi+Psi+Pwi；
Pgmax+Psmax+Pwmax≥1.05Ploadmax；
Wherein: PgrminFor power plant units power output power minimum, unit MW；PgrmaxGo out activity of force for power plant units
Maximum value, unit MW；PiloadFor load power under certain period；
Step S45: carrying out optimizing by particle swarm algorithm, obtain meeting the optimal power solution under constraint condition, further according to
Optimal power result carries out power distribution to thermal power plant.
As further scheme of the invention, steps are as follows for the step S45 particle swarm algorithm optimizing:
Step S51: being generated the population of 20000 particles at random, positioned by the way of coding to each particle,
In, particle represents the performance number of different single thermal power plants；
Step S52: different speed and position are obtained according to each particle, is sought by the constraint condition of step S44
Optimal power solution is sought, the particle for being unsatisfactory for constraint condition is then eliminated；
Step S53: according to seeking the particle rapidity after optimal power solution and position, calculating target function, and according to target
Function size updates population, eliminates the particle for keeping objective function excessive, and generate class according to the speed and position that retain particle
As new particle group；
Step S54: repeating the continuous grey iterative generation new particle group of step S53, until the value of objective function tends towards stability, obtains
This generation population is decoded by minimal solution, that is, optimal solution of objective function, obtains the power distribution solution at each moment.
In conclusion the present invention has following remarkable result due to using above technical scheme, the present invention:
The present invention has fully considered the characteristic that the fluctuation of wind-powered electricity generation online is big and cost is small, is divided to dry season and the wet season two
Period studies geomantic omen fire combined dispatching strategy, and carries out global optimizing to dispatching algorithm using advanced particle swarm algorithm, obtains
Optimal solution carries out power distribution scheduling, improves wind energy utilization, realizes distributing rationally for clean energy resource, reduces coal consumption and hydroelectric power plant
Water is abandoned, flood season is prevented and realizes maximization of economic benefit.
Detailed description of the invention
In order to illustrate more clearly of present example or technical solution in the prior art, to embodiment or will show below
There is in technical description required attached drawing do simply to introduce, it should be apparent that, the accompanying drawings in the following description is only the present invention
Some examples to those skilled in the art, can also be attached according to these under the premise of not paying creativeness
Figure obtains other attached drawings.
Fig. 1 is a kind of geomantic omen fire combined scheduling method block diagram preferentially surfed the Internet based on wind-powered electricity generation.
Fig. 2 is wet season load power figure.
Fig. 3 is wet season thermoelectricity inter-plant power output power distribution figure.
Fig. 4 is wet season geomantic omen thermal power plant power output power distribution figure.
Fig. 5 is dry season load power figure.
Fig. 6 is dry season Hydro-Thermal Systems inter-plant power output power distribution figure.
Fig. 7 is dry season geomantic omen thermal power plant power output power distribution figure.
Specific embodiment
Below in conjunction with the attached drawing in present example, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on hair
Embodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
Such as Fig. 1, a kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation, comprising the following steps:
Step S1: by obtaining wind-powered electricity generation prediction power and load prediction power in scheduling；
Step S2: wind-power electricity generation is preferably all surfed the Internet, and constructs objective function respectively to the power in wet season and dry season
And constraint function, then distribution, which carries out combined dispatching, to be realized to wet season and dry season power；
Step S3: PSO Algorithm objective function is used, seeks optimal solution and tune is allocated to load prediction power
Degree；
Step S4: activity of force is gone out between hydroelectric power plant and thermal power plant according to optimal de-assign.
In the present invention, the dry season realizes that combined dispatching includes the following steps:
Step S21: one day wind-powered electricity generation prediction power and load prediction power are obtained from control centre；
Based on step S22: wind-power electricity generation whole power is all surfed the Internet, and the efficient thermal power plant of fetching portion completely breaks out
Load, then remaining load is distributed into hydroelectric power plant and thermal power plant's progress peak-frequency regulation realization combined dispatching；
Step S23: as follows with thermal power plant and hydroelectric power plant's power swing minimum building objective function:
Δ c=Δ Pg+ Δ Ps；
Wherein: Δ c is that hydroelectric power plant and thermal power plant's power always fluctuate, unit MW；Δ Pg is thermoelectricity field power swing and unit
MW；Δ Ps be hydroelectric power plant's power swing and, Pg is that thermal power plant goes out activity of force, and Ps is that hydroelectric power plant goes out activity of force, and r is machine group number, when i
Segment number；
Step S24: the activity of force bound out and climbing power of single thermal power plant are limited, and to meet geomantic omen fire
Combine that activity of force summation out is equal with load, the power output upper limit of the power of taken power supply requires 1.05 times that are greater than load peak, tool
Body limitation is as follows:
Pgrmin≤Pgri≤Pgrmax；
Piload=Pgi+Psi+Pwi；
Pgmax+Psmax+Pwmax≥1.05Ploadmax；
Wherein: PgrminFor power plant units power output power minimum, unit MW；PgrmaxGo out activity of force for power plant units
Maximum value, unit MW；PwmaxFor maximum wind prediction power, PloadmaxFor maximum wind load prediction power, PiloadFor some time
The lower load power of section；PgiGo out activity of force, Ps for thermal power plant under certain periodiPower plant, which is lauched, for certain period goes out activity of force, PwiFor some time
The lower wind-powered electricity generation prediction power of section；PgmaxFor thermal power plant's power output upper limit of the power, PsmaxFor hydroelectric power plant's power output upper limit of the power.
Step S25: optimizing is carried out by particle swarm algorithm, obtains meeting the optimal power solution under constraint condition, according to most
Excellent power results carry out power distribution to hydroelectric power plant and thermal power plant, wherein in dry season, the particle swarm algorithm optimizing step is such as
Under:
Step S31: being generated the population of 20000 particles at random, positioned by the way of coding to each particle,
In, particle represents the performance number of different single thermal power plant or hydroelectric power plant；
Step S32: different speed and position are obtained according to each particle, is sought most by the constraint condition of step S24
Then excellent power solution eliminates the particle for being unsatisfactory for constraint condition；
Step S33: according to seeking the particle rapidity after optimal power solution and position, calculating target function, and according to target
Function size updates population, eliminates the particle for keeping objective function excessive, and generate class according to the speed and position that retain particle
As new particle group；
Step S34: repeating the continuous grey iterative generation new particle group of step S33, until the value of objective function tends towards stability, obtains
This generation population is decoded by minimal solution, that is, optimal solution of objective function, obtains the power distribution solution at each moment.
As highly preferred embodiment of the present invention, the wet season realizes that combined dispatching includes the following steps:
Step S41: one day wind-powered electricity generation prediction power Pw, load prediction power P are obtained from control centreloadIt is full with hydroelectric power plant
Send out power Ps；
Step S42: wind-power electricity generation whole power is all surfed the Internet, and is allocated to the activity of force that goes out of hydroelectric power plant and thermal power plant
Realize combined dispatching；
Step S43: it is as follows that objective function is constructed at least with thermal power plant's coal consumption:
Wherein, minc is the total consumption of coal of thermal power plant's power, unit t/h；ar,br,crFor thermoelectricity field coal consumption characteristic coefficient；PgriFor
Thermal power plant's generated output, unit MW；R is machine group number；Segment number when i；
Step S44: the activity of force bound out and climbing power of single thermal power plant are limited, and to meet geomantic omen fire
Combine that activity of force summation out is equal with load, the power output upper limit of the power of taken power supply requires 1.05 times that are greater than load peak, tool
Body limitation is as follows:
Pgrmin≤Pgri≤Pgrmax；
Piload=Pgi+Psi+Pwi；
Pgmax+Psmax+Pwmax≥1.05Ploadmax；
Wherein: PgrminFor power plant units power output power minimum, unit MW；PgrmaxGo out activity of force for power plant units
Maximum value, unit MW；PiloadFor load power under certain period；
Step S45: carrying out optimizing by particle swarm algorithm, obtain meeting the optimal power solution under constraint condition, further according to
Optimal power result carries out power distribution to thermal power plant.In the wet season, steps are as follows for the particle swarm algorithm optimizing:
Step S51: being generated the population of 20000 particles at random, positioned by the way of coding to each particle,
In, particle represents the performance number of different single thermal power plants；
Step S52: different speed and position are obtained according to each particle, is sought by the constraint condition of step S44
Optimal power solution is sought, the particle for being unsatisfactory for constraint condition is then eliminated；
Step S53: according to seeking the particle rapidity after optimal power solution and position, calculating target function, and according to target
Function size updates population, eliminates the particle for keeping objective function excessive, and generate class according to the speed and position that retain particle
As new particle group；
Step S54: repeating the continuous grey iterative generation new particle group of step S53, until the value of objective function tends towards stability, obtains
This generation population is decoded by minimal solution, that is, optimal solution of objective function, obtains the power distribution solution at each moment.
In a specific embodiment of the present invention, every the one prediction load power point of acquisition in 15 minutes, as shown in Fig. 2, being
Wet season load power figure illustrates load power variation diagram in one day wet season, and the power of wind power plant is made preferentially to surf the Internet, root
According to 1 wet season of table thermal power plant's property list parameter, take three typical hydroelectric power plants as research object, rated capacity be respectively 260WM,
210MW and 150MW.Furthermore four thermal power plants are taken, capacity is respectively 350MW, 400MW, 400MW and 155MW, four thermal power plants
Determine that coal consumption parameter is respectively a, b, c according to practical, as shown in table 1.
1 wet season of table thermal power plant's property list
| Thermal power plant | No.1 thermal power plant | No. two thermal power plants | No. three thermal power plants | No. four thermal power plants |
| Capacity | 350MW | 400MW | 400MW | 155MW |
| a | 0.00153 | 0.00194 | 0.00195 | 0.00481 |
| b | 10.8616 | 7.4921 | 7.5031 | 10.7367 |
| c | 177.0575 | 310.0021 | 311.9102 | 143.3179 |
After setting objective function and constraint function, calculating analysis is carried out through particle swarm algorithm, obtains objective function most
Thermal power plant under excellent goes out activity of force solution, and Fig. 3 is wet season thermoelectricity inter-plant power output power distribution figure, illustrates one day wet season moderate heat
Power plant contributes power distribution changing condition, and series 1, series 2, series 3 and series 4 go out activity of force for 1 to No. 4 thermal power plants in Fig. 3
Distribution.Wet season power output power distribution is finally obtained to be scheduled.Fig. 4 is wet season geomantic omen thermal power plant power output power distribution figure,
Fig. 4 illustrates one day wet season apoplexy extreme misery power plant power output power distribution changing condition, and series 1 is thermal power output power in Fig. 4
Distribution, series 2 are water power power output power distribution, and series 3 is wind power output power distribution.
In a specific embodiment of the present invention, as shown in figure 5, Fig. 5 is dry season load power figure, dry season one is illustrated
The changing condition of load power in it, and the power for obtaining wind power plant is preferentially surfed the Internet, and is joined according to table 2 dry season thermal power plant's property list
Number takes four thermal power plants and three typical hydroelectric power plants as research object, and the rated capacity and the power output lower limit of the power of thermal power plant are such as
Shown in table 2.Part thermal power plant goes out activity of force close to full hair, and there are certain spare capacities.Therefore when calculating analysis, take 2,3,
It is respectively 370MW, 370MW, 140MW that No. 4 thermal power plants, which go out activity of force, carries out peak regulation together by No. 1 thermal power plant and three hydroelectric power plants.
The difference rated capacity of three hydroelectric power plants is 200WM, 180MW and 120MW, and since hydroelectric power plant's regulating power is strong, there is no climbings
Power limit and minimum load problem, so the power output lower limit of the power is 0MW.
Table 2 dry season thermal power plant's property list
| Thermal power plant | No.1 thermal power plant | No. two thermal power plants | No. three thermal power plants | No. four thermal power plants |
| Capacity | 350MW | 400MW | 400MW | 155MW |
| Power output lower limit | 100MW | 110MW | 110MW | 40MW |
| a | 0.00153 | 0.00194 | 0.00195 | 0.00481 |
| b | 10.8616 | 7.4921 | 7.5031 | 10.7367 |
| c | 177.0575 | 310.0021 | 311.9102 | 143.3179 |
After setting objective function and constraint function, calculating analysis is carried out through particle swarm algorithm, obtains objective function most
Extreme misery power plant power output power solution under excellent, Fig. 6 are dry season Hydro-Thermal Systems inter-plant power output power distribution figures, illustrate dry season one day
Middle extreme misery power plant contributes power distribution changing condition, and the series 1 in Fig. 6 is that No. 1 thermal power plant contributes power distribution, and series 2 is No. 1
The power output power distribution of hydroelectric power plant, series 3 are the power output power distribution of No. 2 hydroelectric power plants, and series 4 is the power output function of No. 3 hydroelectric power plants
Rate distribution.Fig. 7 is dry season geomantic omen thermal power plant power output power distribution figure, illustrates one day dry season apoplexy extreme misery power plant power output function
The changing condition of rate distribution, the series 1 in Fig. 7 are thermal power output power distribution, and series 2 is contributed power distribution for water power, serial 3
For wind power output power distribution.It can be seen from the figure that compared with the wet season, when dry season, utilizes hydroelectric power plant to carry out peak regulation tune more
Frequently, therefore the power output power swing of thermal power plant is smaller, and hydroelectric power plant's fluctuation is larger, successfully alleviates the burden pressure of thermal power plant.
The foregoing is merely the preferred embodiments of invention, are not intended to limit the invention, all in spirit of the invention
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (3)
1. a kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation, it is characterised in that: the following steps are included:
Step S1: wind-powered electricity generation prediction power and load prediction power are obtained by control centre；
Step S2: wind-power electricity generation is preferably all surfed the Internet, and constructs target respectively to the load prediction power in wet season and dry season
Function and constraint function, then distribution, which carries out combined dispatching, to be realized to wet season and dry season load prediction power；
Step S3: PSO Algorithm objective function is used, seeks optimal solution and scheduling is allocated to load prediction power；
Step S4: activity of force is gone out between hydroelectric power plant and thermal power plant according to optimal de-assign；
The dry season realizes that combined dispatching includes the following steps:
Step S21: one day wind-powered electricity generation prediction power and load prediction power are obtained from control centre；
Step S22: wind-power electricity generation whole power is all surfed the Internet, and the efficient thermal power plant of fetching portion completely break out based on it is negative
Lotus, then remaining load is distributed into hydroelectric power plant and thermal power plant's progress peak-frequency regulation realization combined dispatching；
Step S23: as follows with thermal power plant and hydroelectric power plant's power swing minimum building objective function:
Δ c=Δ Pg+ Δ Ps；
Wherein: Δ c is that hydroelectric power plant and thermal power plant's power always fluctuate, unit MW；Δ Pg is thermoelectricity field power swing and unit MW；
Δ Ps be hydroelectric power plant's power swing and, Pg is that thermal power plant goes out activity of force, and Ps is that hydroelectric power plant goes out activity of force, and r is machine group number, i period
Number；
Step S24: the activity of force bound out and climbing power of single thermal power plant are limited, and to meet geomantic omen fire joint
Activity of force summation is equal with load prediction power out, and the power output upper limit of the power of taken power supply requires to be greater than the 1.05 of load peak
Times, concrete restriction is as follows:
Pgrmin≤Pgri≤Pgrmax；
Piload=Pgi+Psi+Pwi；
Pgmax+Psmax+Pwmax≥1.05Ploadmax；
Wherein: PgrminFor power plant units power output power minimum, unit MW；PgrmaxGo out activity of force maximum for power plant units
Value, unit MW；PwmaxFor maximum wind prediction power, PloadmaxFor peak load prediction power, PiloadFor load under certain period
Power；PgiGo out activity of force, Ps for thermal power plant under certain periodiPower plant, which is lauched, for certain period goes out activity of force, PwiFor certain period leeward electricity
Prediction power；PgmaxFor thermal power plant's power output upper limit of the power, PsmaxFor hydroelectric power plant's power output upper limit of the power；
Step S25: optimizing is carried out by particle swarm algorithm, obtains meeting the optimal power solution under constraint condition, according to optimal function
Rate result carries out power distribution to hydroelectric power plant and thermal power plant；
The wet season realizes that combined dispatching includes the following steps:
Step S41: power is completely sent out by wind-powered electricity generation prediction power, load prediction power and the hydroelectric power plant for obtaining one day from control centre；
Step S42: wind-power electricity generation whole power is all surfed the Internet, and is allocated realization to the activity of force that goes out of hydroelectric power plant and thermal power plant
Combined dispatching；
Step S43: it is as follows that objective function is constructed at least with thermal power plant's coal consumption:
Wherein, minc is the total consumption of coal of thermal power plant's power, unit t/h；ar,br,crFor thermoelectricity field coal consumption characteristic coefficient；PgriFor thermoelectricity
Factory's generated output, unit MW；R is machine group number；Segment number when i；
Step S44: the activity of force bound out and climbing power of single thermal power plant are limited, and to meet geomantic omen fire joint
Activity of force summation is equal with load out, and the power output upper limit of the power of taken power supply requires 1.05 times that are greater than load peak, specific limit
It makes as follows:
Pgrmin≤Pgri≤Pgrmax；
Piload=Pgi+Psi+Pwi；
Pgmax+Psmax+Pwmax≥1.05Ploadmax；
Wherein: PgrminFor power plant units power output power minimum, unit MW；PgrmaxGo out activity of force maximum for power plant units
Value, unit MW；PiloadFor load power under certain period；
Step S45: optimizing is carried out by particle swarm algorithm, obtains meeting the optimal power solution under constraint condition, further according to optimal
Power results carry out power distribution to thermal power plant.
2. a kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation according to claim 1, it is characterised in that:
Steps are as follows for the step S25 particle swarm algorithm optimizing:
Step S31: the population of 20000 particles is generated at random, each particle is positioned by the way of coding, wherein
Particle represents the performance number of different single thermal power plant or hydroelectric power plant；
Step S32: obtaining different speed and position according to each particle, seeks optimal function by the constraint condition of step S24
Then rate solution eliminates the particle for being unsatisfactory for constraint condition；
Step S33: according to seeking the particle rapidity after optimal power solution and position, calculating target function, and according to objective function
Size updates population, eliminates the particle for keeping objective function excessive, and is generated with position according to the speed for retaining particle similar
New particle group；
Step S34: repeating the continuous grey iterative generation new particle group of step S33, until the value of objective function tends towards stability, obtains target
This generation population is decoded by minimal solution, that is, optimal solution of function, obtains the power distribution solution at each moment.
3. a kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation according to claim 1, it is characterised in that:
Steps are as follows for the step S45 particle swarm algorithm optimizing:
Step S51: the population of 20000 particles is generated at random, each particle is positioned by the way of coding, wherein
Particle represents the performance number of different single thermal power plants；
Step S52: different speed and position are obtained according to each particle, is sought most by the constraint condition of step S44
Then excellent power solution eliminates the particle for being unsatisfactory for constraint condition；
Step S53: according to seeking the particle rapidity after optimal power solution and position, calculating target function, and according to objective function
Size updates population, eliminates the particle for keeping objective function excessive, and is generated with position according to the speed for retaining particle similar
New particle group；
Step S54: repeating the continuous grey iterative generation new particle group of step S53, until the value of objective function tends towards stability, obtains target
This generation population is decoded by minimal solution, that is, optimal solution of function, obtains the power distribution solution at each moment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610161774.1A CN105868841B (en) | 2016-03-21 | 2016-03-21 | A kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610161774.1A CN105868841B (en) | 2016-03-21 | 2016-03-21 | A kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105868841A CN105868841A (en) | 2016-08-17 |
| CN105868841B true CN105868841B (en) | 2019-07-16 |
Family
ID=56625311
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610161774.1A Active CN105868841B (en) | 2016-03-21 | 2016-03-21 | A kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105868841B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109375507B (en) * | 2018-10-30 | 2021-09-28 | 国网江苏省电力有限公司 | Thermal power generating unit deep peak regulation control method |
| CN109995084B (en) * | 2019-04-24 | 2020-11-06 | 燕山大学 | Cascade hydropower station-thermal power plant combined optimization scheduling method and system |
| CN110909954B (en) * | 2019-12-03 | 2022-10-25 | 西安交通大学 | Multi-stage power supply planning method for maximizing renewable energy utilization |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184472A (en) * | 2011-05-03 | 2011-09-14 | 西安交通大学 | Wind, water and fire united dispatching method based on power grid dispatching side demand |
| WO2013026731A1 (en) * | 2011-08-22 | 2013-02-28 | Abb Technology Ag | System and method to optimize operation of a water network |
| CN104008425A (en) * | 2014-05-12 | 2014-08-27 | 国家电网公司 | Hydro-thermal power system multi-target peak modulation method based on gravity search |
| CN104009494A (en) * | 2014-04-16 | 2014-08-27 | 武汉大学 | Environmental economy power generation dispatching method |
| CN104037755A (en) * | 2013-03-07 | 2014-09-10 | 长沙理工大学 | Optimization method for solving Pareto solution sets of wind-storage-thermal joint operation system in multiple time periods |
-
2016
- 2016-03-21 CN CN201610161774.1A patent/CN105868841B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184472A (en) * | 2011-05-03 | 2011-09-14 | 西安交通大学 | Wind, water and fire united dispatching method based on power grid dispatching side demand |
| WO2013026731A1 (en) * | 2011-08-22 | 2013-02-28 | Abb Technology Ag | System and method to optimize operation of a water network |
| CN104037755A (en) * | 2013-03-07 | 2014-09-10 | 长沙理工大学 | Optimization method for solving Pareto solution sets of wind-storage-thermal joint operation system in multiple time periods |
| CN104009494A (en) * | 2014-04-16 | 2014-08-27 | 武汉大学 | Environmental economy power generation dispatching method |
| CN104008425A (en) * | 2014-05-12 | 2014-08-27 | 国家电网公司 | Hydro-thermal power system multi-target peak modulation method based on gravity search |
Non-Patent Citations (1)
| Title |
|---|
| 考虑风电并网的经济环境联合调度模式及其效果研究;吴隆礼;《中国优秀硕士论文 理工科学辑》;20130301;全文 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105868841A (en) | 2016-08-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102694391B (en) | Day-ahead optimal scheduling method for wind-solar storage integrated power generation system | |
| CN104362677B (en) | A kind of active distribution network distributes structure and its collocation method rationally | |
| CN102097866B (en) | Mid-long-term unit commitment optimizing method | |
| CN109063992A (en) | Consider the power distribution network Expansion Planning method of regional complex energy resource system optimization operation | |
| CN104616069A (en) | Annual power generation plan rolled decomposition optimization method taking balance between plan finishing rate and load rate into consideration | |
| CN105375507A (en) | Power two-stage interactive optimization scheduling system of virtual power plant in haze environment | |
| CN104283236B (en) | The energy storage of a kind of scene is generated electricity by way of merging two or more grid systems intelligent optimization scheduling method | |
| CN109242177B (en) | Active power distribution network planning method | |
| CN104779611A (en) | Economic dispatch method for micro grid based on centralized and distributed double-layer optimization strategy | |
| CN110428103A (en) | A kind of renewable energy energy-storage system collaborative planning method in integrated energy system | |
| CN105528466A (en) | Wind power optimal planning modeling method considering adaptability and economy of power system | |
| CN104158203A (en) | Micro-grid power supply capacity optimization configuration method | |
| CN109523065A (en) | A kind of micro- energy net Optimization Scheduling based on improvement quanta particle swarm optimization | |
| CN104299072A (en) | Security constraint generation schedule planning method based on water and fire coordination | |
| CN110188915A (en) | Energy-storage system Optimal Configuration Method and system in virtual plant based on scene collection | |
| CN109741110A (en) | A kind of wind hydrogen system combined optimization modeling method based on chance constrained programming | |
| CN112701687A (en) | Robust optimization operation method of gas-electricity distribution network system considering price type combined demand response | |
| CN106228462A (en) | A kind of many energy-storage systems Optimization Scheduling based on genetic algorithm | |
| CN104578160B (en) | A kind of microgrid energy control method | |
| CN105868841B (en) | A kind of geomantic omen fire combined scheduling method preferentially surfed the Internet based on wind-powered electricity generation | |
| CN115147245B (en) | Virtual power plant optimal scheduling method for industrial load participating in peak shaving auxiliary service | |
| CN102593855B (en) | Method for stabilizing fluctuation of output power of renewable energy power supply in power system | |
| CN115062869B (en) | Comprehensive energy scheduling method and system considering carbon emission | |
| CN116316640A (en) | Smart power grid dispatching method | |
| CN108075471A (en) | Multi-objective constrained optimization dispatching of power netwoks strategy based on the output prediction of randomness power supply |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |