CN103577893B - A kind of new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity - Google Patents
A kind of new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity Download PDFInfo
- Publication number
- CN103577893B CN103577893B CN201310541224.9A CN201310541224A CN103577893B CN 103577893 B CN103577893 B CN 103577893B CN 201310541224 A CN201310541224 A CN 201310541224A CN 103577893 B CN103577893 B CN 103577893B
- Authority
- CN
- China
- Prior art keywords
- load
- energy
- high energy
- powered
- thermoelectricity
- 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
- 230000005619 thermoelectricity Effects 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000004134 energy conservation Methods 0.000 title claims abstract description 21
- 238000004519 manufacturing process Methods 0.000 claims abstract description 17
- 230000005611 electricity Effects 0.000 claims abstract description 16
- 239000003245 coal Substances 0.000 claims description 36
- 240000002853 Nelumbo nucifera Species 0.000 claims description 33
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims description 33
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims description 33
- 239000002245 particle Substances 0.000 claims description 30
- 238000005457 optimization Methods 0.000 claims description 16
- 241001123248 Arma Species 0.000 claims description 14
- 238000005265 energy consumption Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000006872 improvement Effects 0.000 claims description 5
- 230000001373 regressive effect Effects 0.000 claims description 5
- 230000009977 dual effect Effects 0.000 claims description 4
- 230000009194 climbing Effects 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000009897 systematic effect Effects 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 6
- 238000011161 development Methods 0.000 description 8
- 230000008859 change Effects 0.000 description 4
- 230000029087 digestion Effects 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000001172 regenerating effect Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 239000000809 air pollutant Substances 0.000 description 1
- 231100001243 air pollutant Toxicity 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000013277 forecasting method Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000009987 spinning Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- 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
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Wind Motors (AREA)
Abstract
The invention discloses the two-way energy conservation optimizing method powered for high energy load of a kind of new energy and thermoelectricity, including：Set upWind power data in following preset time period are carried out ultra-short term power prediction by model；Ultra-short term is carried out to the high energy load data in following preset time period using existing load prediction mathematical modeling；The need for judging whether wind power meets the normal production run of high energy load in following preset time period；If the need for wind power disclosure satisfy that the normal production run of high energy load in following preset time period, using wind-powered electricity generation and directly being powered for high energy load；If the need for wind power can not meet the normal production run of high energy load in following preset time period, setting up new energy and the two-way energy saving optimizing model powered for high energy load of thermoelectricity.The new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, it is possible to achieve reliability is high, energy utilization rate is high and the advantage of energy-saving effect difference.
Description
Technical field
The present invention relates to new-energy grid-connected technical field, in particular it relates to which it is high energy that a kind of new energy is two-way with thermoelectricity
The energy conservation optimizing method that load is powered.
Background technology
21 century, with the development and the continuous rising of energy demand of social economy, countries in the world are increased to coal, stone
Oil, the use dynamics of natural gas, because reserves are limited and exploitation increasingly sharpens, will can cause the exhaustion of these resources.Together
When, combustion of fossil fuels can produce the continued emissions of the air pollutants such as sulfur dioxide, particulate, particularly carbon dioxide,
The greenhouse effects of the earth will be aggravated, cause air pollution, be negatively affected to our living environment.Global energy crisis and
Environmental destruction turns into the problem of concern human survival and development, how to save using the energy, improve its utilization ratio into
For various countries' focus of attention.
Power industry, as national basis industry, is that the main primary energy of China directly utilizes industry, power industry
The efficient utilization of tremendous development and electric power, is social economy's progress and the basic guarantee of Society.Electric power energy-saving, it is first
First more strong Sustainable development policies should be taken in terms of energy exploitation and application, energetically from national energy strategy
Develop regenerative resource, adjustment and optimization energy industry structure, realization saving under development；Secondly, energy conversion and profit are improved
With efficiency, in fields such as production, transmission and consumption, by taking the comprehensive measures such as technology, law, economy and administration, improve
Efficiency of energy utilization, maximum economic and social benefit is obtained with minimum resource consumption.
Wind-power electricity generation, as a kind of important renewable energy forms, is that technology is most ripe, most in current regenerative resource
Have one of scale exploit condition and the generation mode of commercialized development prospect.China's wind-powered electricity generation mainly concentrates access using extensive
Mode, due to the limitation that wind resource is distributed, wind power plant is built in the end of power network mostly, and network structure is weaker, wind-powered electricity generation
Ability to send outside is limited, it is necessary to which emphasis considers the on-site elimination of wind-powered electricity generation.Develop power load, on-site elimination wind-powered electricity generation, can directly by
Local wind power resources advantage is converted into local economy advantage, is that the sustainable development of Wind Power Generation Industry provides safeguard.And develop medium and small enterprise
Industry, power load is smaller, acts on little to the wind-powered electricity generation for a large amount of affluences of dissolving, therefore, and emphasis considers that development power load is larger
High energy industry will be more efficient come wind-powered electricity generation of dissolving.But because wind energy has randomness and intermittent feature so that wind-powered electricity generation is
High energy load is powered with certain uncertainty, so, it is considered to a kind of new energy is two-way for the confession of high energy load with thermoelectricity
The energy saving optimizing scheme of electricity is particularly important.
During the present invention is realized, inventor has found at least to exist in the prior art that reliability is low, energy utilization rate
The defects such as low and energy-saving effect difference.
The content of the invention
It is an object of the present invention in view of the above-mentioned problems, propose that a kind of new energy is two-way for the confession of high energy load with thermoelectricity
The energy conservation optimizing method of electricity, to realize high reliability, energy utilization rate height and the advantage of energy-saving effect difference.
To achieve the above object, the technical solution adopted by the present invention is：A kind of new energy and thermoelectricity it is two-way for high energy it is negative
The energy conservation optimizing method that lotus is powered, including：
Step 1：With reference to the randomness and fluctuation of wind, according to history wind power data, pass through curve matching and parameter
Estimation, sets up arma modeling, and ultra-short term power prediction is carried out to wind power data in following preset time period；
Step 2：According to the running situation of high energy load, when being preset using existing load prediction mathematical modeling to future
Between high energy load data in section carry out ultra-short term；
Step 3：According to the prediction data of wind power and the prediction data of high energy load, following preset time period is judged
The need for whether interior wind power meets the normal production run of high energy load；
Step 4：The need for if wind power disclosure satisfy that the normal production run of high energy load in following preset time period,
Wind-powered electricity generation is then used directly to be powered for high energy load；
Step 5：The need for if wind power can not meet the normal production run of high energy load in following preset time period,
Then to use the two-way fired power generating unit energy consumption saved for high energy load power supply station of new energy and thermoelectricity to be target to the maximum, synthesis is examined
Consider various constraintss, set up new energy and the two-way energy saving optimizing model powered for high energy load of thermoelectricity.
Further, after the step 5, in addition to：
Step 6：The energy saving optimizing model in step 4 is solved using particle cluster algorithm is improved, new energy is finally given
Source and the two-way energy saving optimizing scheme powered for high energy load of thermoelectricity；
In step 6, the improvement particle cluster algorithm, refers to make full use of the global search of particle cluster algorithm and former-right
The above-mentioned energy saving optimizing model of local optimal searching capacity calculation of even interior point method.
Further, in step 6, it is described that the energy saving optimizing model in step 4 is carried out using improvement particle cluster algorithm
The operation of solution, is specifically included：
Particle cluster algorithm is used in the starting stage of optimization, by the particle group optimizing knot close to global optimum of certain number of times
Fruit obtains more excellent solution as the further local optimum of initial value of primal dual interior point method.
Further, in step 6, the particle group optimizing result close to global optimum using certain number of times as
The further local optimum of initial value of primal dual interior point method, obtains the operation of more excellent solution, specifically includes：
When containing the Large-scale Optimization Problems of equation and inequality constraints with PSO Algorithm, adjusted using feasible robustness
Strategy, is adjusted to the particle for being unsatisfactory for equality constraint every time；
For multi-modal optimization problem, for inactive particle, i.e., do not become substantially close to population optimal value for continuous n times
The particle of change, is reinitialized to avoid precocious generation to its speed；N is natural number；
The solution obtained using after global search is as initial value, for continuously differentiable Optimized model after smoothing, from convergence
Stability of characteristics and change little nonlinear preprocessing with the increase iterations of calculation scale and carry out local optimum,
Obtain the higher-quality solution of optimization problem.
Further, in step 1, it is described pre- to wind power data progress ultra-short term power in following preset time period
Survey, be specially：
Ultra-short term wind energy Forecasting Methodology based on time series, the time series data obtained according to systematic observation passes through
Curve matching and parameter Estimation carry out founding mathematical models, and then predict with this mathematical modeling the data in future.
Further, in the ultra-short term wind energy Forecasting Methodology of the time series, using auto regressive moving average ARMA
Model is predicted as the model of time series to wind power；
The structure of the auto regressive moving average arma modeling is as follows：
In formula, XtIt is ARMA (p, a q) process for the time series of wind power；ajFor AR parameters；bkJoin for MA
Number；et-kTo represent the time series of white-noise process；P and q are respectively AR exponent numbers and MA exponent numbers.
Further, in step 2, it is described to use existing load prediction mathematical modeling in following preset time period
High energy load data carries out the operation of ultra-short term, specifically includes：
5min to 60min ultra-short term, its prediction principle is to utilize existing historical data, using appropriate
Mathematical forecasting model is to predicting that the load value of day is estimated；The historical data includes history daily load data and meteorological number
According to.
Further, in steps of 5, the new energy and the two-way energy saving optimizing mould powered for high energy load of thermoelectricity
Type, be specially：
To use the two-way fired power generating unit energy consumption saved for high energy load power supply station of new energy and thermoelectricity to be target to the maximum,
Consider the Optimized model of a variety of constraintss such as power constraint, fired power generating unit technology units limits.
Further, it is described with use new energy and thermoelectricity two-way for the saving of high energy load power supply station fired power generating unit energy
Consumption is target to the maximum, considers the Optimized models of a variety of constraintss such as power constraint, fired power generating unit technology units limits
Operation, is specifically included：
It is considered as new energy and the mesh of the two-way fired power generating unit energy consumption maximum saved for high energy load power supply station of thermoelectricity
Scalar functions, its mathematical description is as follows：
max Csave；
In formula,
Csave=C1-C2；
Fired power generating unit can be divided into according to the operation characteristic of fired power generating unit by base lotus unit and economic load dispatching unit；
When being powered only with fired power generating unit for high energy load, because high energy workload demand is relatively stable, load becomes
Change is small, therefore the base lotus unit that coal consumption amount can be used low is powered for high energy load；When double with fired power generating unit using new energy
To when being powered for high energy load, although new energy does not consume primary energy, but the wave characteristic of new energy needs coal consumption amount
The base lotus unit that higher economic load dispatching unit replaces coal consumption amount low undertakes corresponding load；
CsaveTo use the two-way fired power generating unit coal consumption amount saved for high energy load power supply station of new energy and thermoelectricity；C1For
The coal consumption amount for using fired power generating unit to be powered for high energy load；C2To use new energy and thermoelectricity are two-way to be powered for high energy load
Coal consumption amount；For the coal consumption flow function of base lotus unit,It is base lotus unit i in period t power output, aiWith
biFor base lotus unit i coal consumption amount characteristic coefficient, G1For base lotus unit number；For the coal consumption amount of economic load dispatching unit
Function,It is economic load dispatching unit j in period t power output, a'jAnd b'jIt is special for economic load dispatching unit j coal consumption amount
Property coefficient；G2For economic load dispatching unit number；The wind power prediction value dissolved for high energy load.
Further, the bound for objective function, including：
（1）System power Constraints of Equilibrium：
In formula,For predicted load；
（2）Conventional power unit is constrained
Generating set units limits：
In formula, pmin,i, pmax,iRespectively base lotus unit i minimum load and EIAJ；pmin,j, pmax,jRespectively pass through
Ji scheduling unit j minimum load and EIAJ；
（3）Generating set climbing rate is constrained：
In formula, rd,i, ru,iRespectively base lotus unit i descending Ramp Rate and up Ramp Rate；rd,j, ru,jRespectively
Economic load dispatching unit j descending Ramp Rate and up Ramp Rate.
The new energy of various embodiments of the present invention and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, due to bag
Include：Arma modeling is set up, ultra-short term power prediction is carried out to wind power data in following preset time period；Using existing negative
Lotus mathematical prediction model carries out ultra-short term to the high energy load data in following preset time period；Judge following pre-
If the need for whether wind power meets the normal production run of high energy load in the period；If wind-powered electricity generation in following preset time period
The need for power disclosure satisfy that the normal production run of high energy load, then wind-powered electricity generation is used directly to be powered for high energy load；If not
The need for carrying out wind power in preset time period and can not meeting the normal production run of high energy load, then new energy and thermoelectricity are set up
The two-way energy saving optimizing model powered for high energy load；Existing high energy supplying charge method can be solved to be difficult to improve new energy
Source digestion capability and can not effectively save primary energy the problem of, improve new energy level of dissolving, effectively save and once can
Source；So as to overcome the defect that reliability is low, energy utilization rate is low in the prior art and energy-saving effect is poor, to realize reliability
High and energy-saving effect difference the advantage of high, energy utilization rate.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
Obtain it is clear that or being understood by implementing the present invention.
Below by drawings and examples, technical scheme is described in further detail.
Brief description of the drawings
Accompanying drawing is used for providing a further understanding of the present invention, and constitutes a part for specification, the reality with the present invention
Applying example is used to explain the present invention together, is not construed as limiting the invention.In the accompanying drawings：
To be new energy of the present invention with thermoelectricity two-way is that the flow of the energy conservation optimizing method that high energy load is powered is illustrated by Fig. 1
Figure；
Fig. 2 be new energy of the present invention it is two-way with thermoelectricity be that wind power is pre- in the energy conservation optimizing method that high energy load is powered
Mapping；
To be new energy of the present invention two-way with thermoelectricity is IEEE39 nodes in the energy conservation optimizing method that high energy load is powered by Fig. 3
The power network wiring schematic diagram of system.
Embodiment
The preferred embodiments of the present invention are illustrated below in conjunction with accompanying drawing, it will be appreciated that preferred reality described herein
Apply example to be merely to illustrate and explain the present invention, be not intended to limit the present invention.
It is difficult to improve new energy digestion capability and effectively save once can not can for existing high energy supplying charge method
The problem of source, according to embodiments of the present invention, as shown in Figure 1-Figure 3 there is provided a kind of new energy and thermoelectricity it is two-way for high energy it is negative
The energy conservation optimizing method that lotus is powered, improves the level of dissolving of new energy, has effectively saved primary energy.
The new energy of the present embodiment and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, including：
Step 1：With reference to the randomness and fluctuation of wind, according to history wind power data, pass through curve matching and parameter
Estimation, sets up arma modeling, and ultra-short term power prediction is carried out to wind power data in following preset time period；
In step 1, wind power prediction refers to：Ultra-short term wind energy Forecasting Methodology based on time series, when should be based on
Between sequence the time series data that is obtained according to systematic observation of ultra-short term wind energy Forecasting Methodology, estimated by curve matching and parameter
Meter carrys out founding mathematical models, and then predicts with this mathematical modeling the data in future.The types of models of time series is a lot, here
Using auto regressive moving average（ARMA）Model is predicted to wind power.ARMA (p, q) model structure is as follows：
In formula, XtIt is ARMA (p, a q) process for the time series of wind power；ajFor AR parameters；bkJoin for MA
Number；et-kTo represent the time series of white-noise process；P and q are respectively AR exponent numbers and MA exponent numbers；
Step 2：According to the running situation of high energy load, when being preset using existing load prediction mathematical modeling to future
Between high energy load data in section carry out ultra-short term；
In step 2, load prediction refers to：5min to 60min ultra-short term, its prediction principle is using existing
Some historical datas（History daily load data and meteorological data etc.）, using load of the appropriate mathematical forecasting model to prediction day
Value is estimated；
In step 2, load prediction mathematical modeling is techniques well known, current existing Short-term Load Forecasting Model
Mainly there are time series predicting model, Regression Model, Artificial Neural Network Prediction Model, wavelet analysis forecast model
Deng.Bibliography：《The research of power-system short-term load forecasting method based on load decomposition》[master thesis], Wang Cheng
Guiding principle, Hebei：North China Electric Power University, 2006；
Step 3：According to the prediction data of wind power and the prediction data of high energy load, following preset time period is judged
The need for whether interior wind power meets the normal production run of high energy load；
Step 4：The need for if wind power disclosure satisfy that the normal production run of high energy load in following preset time period,
Wind-powered electricity generation is then used directly to be powered for high energy load；
Step 5：The need for if wind power can not meet the normal production run of high energy load in following preset time period,
Then to use the two-way fired power generating unit energy consumption saved for high energy load power supply station of new energy and thermoelectricity to be target to the maximum, synthesis is examined
Consider various constraintss, set up new energy and the two-way energy saving optimizing model powered for high energy load of thermoelectricity；
In steps of 5, it is that the energy saving optimizing model that high energy load is powered refers to using new that new energy is two-way with thermoelectricity
The energy is target to the maximum with the two-way fired power generating unit energy consumption saved for high energy load power supply station of thermoelectricity, considers power network about
The Optimized model of a variety of constraintss such as beam, fired power generating unit technology units limits.Specifically include：
It is considered as new energy and the mesh of the two-way fired power generating unit energy consumption maximum saved for high energy load power supply station of thermoelectricity
Scalar functions, its mathematical description is as follows：
max Csave；
In formula,
Csave=C1-C2；
Fired power generating unit can be divided into according to the operation characteristic of fired power generating unit by base lotus unit and economic load dispatching unit.Base lotus unit
Coal consumption amount it is low, dynamic response capability is poor, the less base lotus of appropriate bands load variations；The dynamic response capability of economic load dispatching unit
By force, coal consumption amount is higher, is suitable as spinning reserve, participatory economy scheduling or frequency modulation etc..When negative for high energy only with fired power generating unit
When lotus is powered, because high energy workload demand is relatively stable, load variations are small, therefore can be using the low base lotus unit of coal consumption amount
Powered for high energy load；When use new energy with fired power generating unit is two-way powered for high energy load when, although new energy does not disappear
Primary energy is consumed, but the wave characteristic of new energy needs the higher economic load dispatching unit of coal consumption amount to replace the low base lotus of coal consumption amount
Unit undertakes corresponding load.CsaveTo use the two-way fired power generating unit saved for high energy load power supply station of new energy and thermoelectricity
Coal consumption amount；C1To use the coal consumption amount that fired power generating unit is powered for high energy load；C2To use new energy and thermoelectricity two-way for high load
The coal consumption amount that energy load is powered；For the coal consumption flow function of base lotus unit,For base lotus unit i period t output
Power, aiAnd biFor base lotus unit i coal consumption amount characteristic coefficient, G1For base lotus unit number；For economic load dispatching unit
Coal consumption flow function,It is economic load dispatching unit j in period t power output, a'jAnd b'jFor economic load dispatching unit j coal consumption
Flow characteristic coefficient；G2For economic load dispatching unit number；The wind power prediction value dissolved for high energy load.
Above-mentioned bound for objective function is：
（1）System power Constraints of Equilibrium：
In formula,For predicted load.
（2）Conventional power unit is constrained
Generating set units limits：
In formula, pmin,i, pmax,iRespectively base lotus unit i minimum load and EIAJ；pmin,j, pmax,jRespectively pass through
Ji scheduling unit j minimum load and EIAJ.
（3）Generating set climbing rate is constrained：
In formula, rd,i, ru,iRespectively base lotus unit i descending Ramp Rate and up Ramp Rate；rd,j, ru,jRespectively
Economic load dispatching unit j descending Ramp Rate and up Ramp Rate；
Step 6：The energy saving optimizing model in step 4 is solved using particle cluster algorithm is improved, new energy is finally given
Source and the two-way energy saving optimizing scheme powered for high energy load of thermoelectricity；
In step 6, particle cluster algorithm is improved to refer to：Make full use of in the global search and original-antithesis of particle cluster algorithm
The above-mentioned energy saving optimizing model of local optimal searching capacity calculation of point method.Particle cluster algorithm is used in the starting stage of optimization, will be certain
The particle group optimizing result close to global optimum of number of times as primal dual interior point method the further local optimum of initial value so that
Obtain more excellent solution.
In step 6, can when containing the Large-scale Optimization Problems of equation and inequality constraints using PSO Algorithm
Row domain generally than narrow, be difficult to the requirement for quickly meeting institute's Constrained especially equality constraint, therefore, used in optimization process
Feasible robustness adjustable strategies, are adjusted to the particle for being unsatisfactory for equality constraint every time.
In step 6, for multi-modal optimization problem, particle cluster algorithm is likely to occur precocious phenomenon so that optimization is absorbed in office
Portion is optimal, therefore, for inactive particle, i.e., the particle not changed substantially close to population optimal value for continuous n times, to it
Speed is reinitialized to avoid precocious generation.
In step 6, the solution obtained using after global search is as initial value, for continuously differentiable optimization mould after smoothing
Type, from convergence property stabilization and changes little nonlinear preprocessing with the increase iterations of calculation scale and enters
Row local optimum, so as to obtain the higher-quality solution of optimization problem.
According to the two-way energy conservation optimizing method powered for high energy load of the new energy and thermoelectricity of above-described embodiment, with
IEEE39 node systems are analyzed as follows as Knowledge Verification Model：
It is high energy supplying charge formula by contrasting only with fired power generating unit and uses new energy and thermoelectricity two-way for height
Carry energy supplying charge formula, it can be seen that when being powered only with fired power generating unit for high energy load, the coal consumption amount of fired power generating unit
For 520.178t, and use new energy with thermoelectricity is two-way powered for high energy load when, the coal consumption amount of fired power generating unit is
495.154t, saves 25.024t coal consumption amount, so as to be effectively reduced the primary energy consumption amount of system.
Examples detailed above analysis shows：It is that high energy load is powered that the new energy of the various embodiments described above of the present invention is two-way with thermoelectricity
Energy conservation optimizing method, solving existing high energy supplying charge method and being difficult to improve new energy digestion capability and can not effectively save
About primary energy the problem of, new energy and thermoelectricity bidirectional power supply are established on the basis of load prediction and wind power prediction
Energy saving optimizing model, and corresponding energy saving optimizing scheme is given, so as to improve the level of dissolving of new energy, effectively save
Primary energy.
In summary, it is that the energy-conservation that high energy load is powered is excellent that the new energy of the various embodiments described above of the present invention is two-way with thermoelectricity
Change method, including：According to history wind power data, by arma modeling come to wind power data in following preset time period
Carry out ultra-short term power prediction；According to the running situation of high energy load, using existing load prediction mathematical modeling to future
High energy load data in preset time period carries out ultra-short term；When wind power in following preset time period can not
The need for meeting the normal production run of high energy load, then to use new energy and thermoelectricity two-way for high energy load power supply station section
Fired power generating unit energy consumption about is target to the maximum, considers various constraintss, and it is high energy to set up new energy two-way with thermoelectricity
The energy saving optimizing model that load is powered, and using improve particle cluster algorithm the model is solved, finally give new energy and
The two-way energy saving optimizing scheme powered for high energy load of thermoelectricity.
Finally it should be noted that：The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention,
Although the present invention is described in detail with reference to the foregoing embodiments, for those skilled in the art, it still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic.
Within the spirit and principles of the invention, any modification, equivalent substitution and improvements made etc., should be included in the present invention's
Within protection domain.
Claims (7)
1. a kind of new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, it is characterised in that including：
Step 1：With reference to the randomness and fluctuation of wind, according to history wind power data, by curve matching and parameter Estimation,
Arma modeling is set up, ultra-short term power prediction is carried out to wind power data in following preset time period；
Step 2：According to the running situation of high energy load, using existing load prediction mathematical modeling to following preset time period
Interior high energy load data carries out ultra-short term；
Step 3：According to the prediction data of wind power and the prediction data of high energy load, wind in following preset time period is judged
The need for whether electrical power meets the normal production run of high energy load；
Step 4：If the need for wind power disclosure satisfy that the normal production run of high energy load in following preset time period, adopting
Directly powered with wind-powered electricity generation for high energy load；
Step 5：The need for if wind power can not meet the normal production run of high energy load in following preset time period, with
Use new energy to be target to the maximum with the two-way fired power generating unit energy consumption saved for high energy load power supply station of thermoelectricity, consider each
Constraints is planted, new energy and the two-way energy saving optimizing model powered for high energy load of thermoelectricity is set up；
In steps of 5, the new energy and the two-way energy saving optimizing model powered for high energy load of thermoelectricity, be specially：
It is comprehensive to use the two-way fired power generating unit energy consumption saved for high energy load power supply station of new energy and thermoelectricity to be target to the maximum
Consider power constraint, the Optimized model of a variety of constraintss of fired power generating unit technology units limits；
It is described to be target to the maximum to use new energy and thermoelectricity two-way for the fired power generating unit energy consumption of high energy load power supply station saving,
Consider power constraint, the operation of the Optimized model of a variety of constraintss of fired power generating unit technology units limits, specifically include：
It is considered as new energy and the target letter of the two-way fired power generating unit energy consumption maximum saved for high energy load power supply station of thermoelectricity
Number, its mathematical description is as follows：
max Csave；
In formula,
Csave=C1-C2；
Fired power generating unit can be divided into according to the operation characteristic of fired power generating unit by base lotus unit and economic load dispatching unit；
When being powered only with fired power generating unit for high energy load, because high energy workload demand is relatively stable, load variations are small,
Therefore the base lotus unit that coal consumption amount can be used low is powered for high energy load；When using, new energy and fired power generating unit are two-way for height
When load energy load is powered, although new energy does not consume primary energy, but the wave characteristic of new energy needs coal consumption amount higher
The base lotus unit that economic load dispatching unit replaces coal consumption amount low undertakes corresponding load；
CsaveTo use the two-way fired power generating unit coal consumption amount saved for high energy load power supply station of new energy and thermoelectricity；C1To use
Fired power generating unit is the coal consumption amount that high energy load is powered；C2To use new energy and the two-way coal powered for high energy load of thermoelectricity
Consumption；For the coal consumption flow function of base lotus unit,It is base lotus unit i in period t power output, aiAnd biFor base
Lotus unit i coal consumption amount characteristic coefficient, G1For base lotus unit number；For the coal consumption flow function of economic load dispatching unit,It is economic load dispatching unit j in period t power output, a'jAnd b'jFor economic load dispatching unit j coal consumption amount characteristic coefficient；
G2For economic load dispatching unit number；The wind power prediction value dissolved for high energy load, T is the period total in calculating cycle
Number, g represents fired power generating unit；
The bound for objective function, including：
(1) system power Constraints of Equilibrium：
In formula,For predicted load；
(2) conventional power unit is constrained
Generating set units limits：
In formula, pmin,i, pmax,iRespectively base lotus unit i minimum load and EIAJ；pmin,j, pmax,jIt is respectively economical to adjust
Spend unit j minimum load and EIAJ；
(3) generating set climbing rate is constrained：
In formula, rd,i, ru,iRespectively base lotus unit i descending Ramp Rate and up Ramp Rate；rd,j, ru,jIt is respectively economical
Dispatch unit j descending Ramp Rate and up Ramp Rate.
2. new energy according to claim 1 and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, it is special
Levy and be, after the step 5, in addition to：
Step 6：Using improve particle cluster algorithm the energy saving optimizing model in step 5 is solved, finally give new energy and
The two-way energy saving optimizing scheme powered for high energy load of thermoelectricity；
In step 6, in the improvement particle cluster algorithm, the global search and original-antithesis for referring to make full use of particle cluster algorithm
The above-mentioned energy saving optimizing model of local optimal searching capacity calculation of point method.
3. new energy according to claim 2 and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, it is special
Levy and be, in step 6, the operation solved using improvement particle cluster algorithm to the energy saving optimizing model in step 5,
Specifically include：
Particle cluster algorithm is used in the starting stage of optimization, the particle group optimizing result close to global optimum of certain number of times is made
For the further local optimum of initial value of primal dual interior point method, more excellent solution is obtained.
4. new energy according to claim 3 and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, it is special
Levy and be, it is in step 6, described to regard the particle group optimizing result close to global optimum of certain number of times as point in original-antithesis
The further local optimum of initial value of method, obtains the operation of more excellent solution, specifically includes：
When containing the Large-scale Optimization Problems of equation and inequality constraints with PSO Algorithm, plan is adjusted using feasible robustness
Slightly, the particle for being unsatisfactory for equality constraint is adjusted every time；
For multi-modal optimization problem, for inactive particle, i.e., do not changed substantially close to population optimal value for continuous n times
Particle, is reinitialized to avoid precocious generation to its speed；N is natural number；
The solution obtained using after global search is as initial value, for continuously differentiable Optimized model after smoothing, from convergence property
Stable and with calculation scale increase iterations changes little nonlinear preprocessing and carries out local optimum, obtains
The higher-quality solution of optimization problem.
5. new energy and the two-way energy saving optimizing powered for high energy load of thermoelectricity according to any one of claim 1-4
Method, it is characterised in that in step 1, it is described pre- to wind power data progress ultra-short term power in following preset time period
Survey, be specially：
Ultra-short term wind energy Forecasting Methodology based on time series, the time series data obtained according to systematic observation passes through curve
Fitting and parameter Estimation carry out founding mathematical models, and then predict with this mathematical modeling the data in future.
6. new energy according to claim 5 and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity, it is special
Levy and be, in step 1, in the ultra-short term wind energy Forecasting Methodology of the time series, using auto regressive moving average ARMA
Model is predicted as the model of time series to wind power；
The structure of the auto regressive moving average arma modeling is as follows：
In formula, XtIt is ARMA (p, a q) process for the time series of wind power；ajFor AR parameters；bkFor MA parameters；
et-kTo represent the time series of white-noise process；P and q are respectively AR exponent numbers and MA exponent numbers；T is prediction time, and j is 1 to p's
Natural number (j=1,2 ... p), k for 1 to q natural number (k=1,2 ... q).
7. new energy and the two-way energy saving optimizing powered for high energy load of thermoelectricity according to any one of claim 1-4
Method, it is characterised in that in step 2, it is described to use existing load prediction mathematical modeling in following preset time period
High energy load data carries out the operation of ultra-short term, specifically includes：
5min to 60min ultra-short term, its prediction principle is to utilize existing historical data, using appropriate mathematics
Forecast model is to predicting that the load value of day is estimated；The historical data includes history daily load data and meteorological data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310541224.9A CN103577893B (en) | 2013-11-05 | 2013-11-05 | A kind of new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310541224.9A CN103577893B (en) | 2013-11-05 | 2013-11-05 | A kind of new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103577893A CN103577893A (en) | 2014-02-12 |
| CN103577893B true CN103577893B (en) | 2017-09-05 |
Family
ID=50049636
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310541224.9A Active CN103577893B (en) | 2013-11-05 | 2013-11-05 | A kind of new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103577893B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103916071B (en) * | 2014-04-16 | 2016-03-02 | 国家电网公司 | A kind of uniform output intelligent control system of wind light mutual complementing power generation and method |
| CN106844594A (en) * | 2017-01-12 | 2017-06-13 | 南京大学 | A kind of electric power Optimal Configuration Method based on big data |
| CN107797060B (en) * | 2017-03-07 | 2020-04-10 | 湖南嘉飞达电子科技有限公司 | Motor system energy efficiency detection method and system |
| CN108899913B (en) * | 2018-08-17 | 2021-08-31 | 广东电网有限责任公司 | Real-time monitoring and index automatic decomposition method for power grid section |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102130497A (en) * | 2011-04-10 | 2011-07-20 | 江苏香江科技股份有限公司 | Power supply device for wind-light complementation of outdoor machine room and method thereof |
| CN102170129A (en) * | 2011-04-22 | 2011-08-31 | 武汉大学 | Low-carbon dispatching method and device for electric power system based on large-scale wind power grid connection |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5167106B2 (en) * | 2008-12-22 | 2013-03-21 | 株式会社日立エンジニアリング・アンド・サービス | Wind power plant and its power generation control method |
| CN201369575Y (en) * | 2008-12-26 | 2009-12-23 | 西北电网有限公司 | Wind power dispatching decision support device |
| CN102102626B (en) * | 2011-01-30 | 2012-08-29 | 华北电力大学 | Method for forecasting short-term power in wind power station |
| CN102779322B (en) * | 2012-06-29 | 2015-07-29 | 西安理工大学 | Based on thermal power plant's integrated dispatch method of environmental protection and economic benefit |
| CN102880810B (en) * | 2012-10-25 | 2015-07-15 | 山东电力集团公司电力科学研究院 | Wind power prediction method based on time sequence and neural network method |
-
2013
- 2013-11-05 CN CN201310541224.9A patent/CN103577893B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102130497A (en) * | 2011-04-10 | 2011-07-20 | 江苏香江科技股份有限公司 | Power supply device for wind-light complementation of outdoor machine room and method thereof |
| CN102170129A (en) * | 2011-04-22 | 2011-08-31 | 武汉大学 | Low-carbon dispatching method and device for electric power system based on large-scale wind power grid connection |
Non-Patent Citations (3)
| Title |
|---|
| "含风电场的电力系统动态经济调度问题研究";周玮;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20110515;摘要，第2章第2.3-2.4节，第3章第3.2节 * |
| "基于多目标优化的含风电场的电力系统经济调度";常鹏;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20130615;摘要，第2章第2.2节 * |
| "甘肃酒泉风电就地消纳策略";周有学;《电力需求侧管理》;20130131;第15卷(第1期);摘要，第2-3节 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103577893A (en) | 2014-02-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gao et al. | Optimal dispatching of wind-PV-mine pumped storage power station: A case study in Lingxin Coal Mine in Ningxia Province, China | |
| CN103762589B (en) | A kind of new forms of energy capacity ratio hierarchy optimization method in electrical network | |
| CN103049798B (en) | A kind of short-term power generation power Forecasting Methodology being applied to photovoltaic generating system | |
| Capizzi et al. | Recurrent neural network-based control strategy for battery energy storage in generation systems with intermittent renewable energy sources | |
| CN107910863A (en) | Consider the power distribution network dispatching method that photovoltaic is contributed with workload demand forecast interval | |
| CN107153885A (en) | Consider the real-time generation schedule optimization method of fired power generating unit depth peak regulation | |
| CN114021390A (en) | Random robust optimization method for urban comprehensive energy system and application thereof | |
| CN104362681B (en) | A kind of isolated island micro-capacitance sensor capacity configuration optimizing method considering randomness | |
| Wadi et al. | Smart hybrid wind-solar street lighting system fuzzy based approach: Case study Istanbul-Turkey | |
| CN103577893B (en) | A kind of new energy and the two-way energy conservation optimizing method powered for high energy load of thermoelectricity | |
| CN105576709A (en) | Hybrid algorithm based optimization method for wind power-pumped unified operation | |
| Carta et al. | Optimal sizing of stand-alone wind-powered seawater reverse osmosis plants without use of massive energy storage | |
| Ma et al. | Optimal capacity configuration of hydro-wind-PV hybrid system and its coordinative operation rules considering the UHV transmission and reservoir operation requirements | |
| Yildiz | Turkey's energy policy, regional role and future energy vision | |
| CN109888770A (en) | Wind energy turbine set installed capacity optimization method based on chance constrained programming and fluctuation cost | |
| CN101908177A (en) | Method for regulating and controlling carbon emission of users by using intelligent power grid | |
| CN106684913A (en) | Energy storage power station tracking generation plan control system and method based on multiple agents | |
| CN112183841A (en) | Optimized dispatching method of micro-grid containing electric automobile based on simulated annealing algorithm | |
| CN107545327A (en) | Photovoltaic generation short-term output power Comprehensive Prediction Method based on SVMs | |
| CN113193554B (en) | New energy ship energy saving and emission reduction method and system based on data driving | |
| CN106712060A (en) | Multi-agent-based hundred-megawatt level battery energy storage system control method and system | |
| Wang et al. | Research on capacity optimization and real-time control of island microgrid considering time-shifting load | |
| Wang et al. | Research on short‐term and mid‐long term optimal dispatch of multi‐energy complementary power generation system | |
| Zhang et al. | Energy economy and energy structure optimization countermeasures based on big data mining | |
| Li et al. | Low carbon unit commitment for power system with wind farms and carbon capture devices based on DE-BBO algorithm |
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 |