CN109713720A - A kind of balance of electric power and ener method of new-energy grid-connected operation - Google Patents
A kind of balance of electric power and ener method of new-energy grid-connected operation Download PDFInfo
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- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- 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
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Abstract
A kind of balance of electric power and ener method of new-energy grid-connected operation.The characteristics such as randomness and uncontrollability first against new energy establish new energy power output model.Extensive new energy volume metering based on reliability assessment such as calculates at the honourable unit capacity size that can be considered as conventional power generation unit under the premise of reliabilities.Using particle swarm algorithm is used, is considered in power system security constraints and the smallest situation of system cost of electricity-generating at the same time, obtain the power output scheme of each generating set.
Description
Technical field
The present invention relates to a kind of balance of electric power and ener methods of new-energy grid-connected.
Background technique
The pollution problem of conventional energy resource and lack of energy problem restrict the development of conventional energy resource generation technology, in recent years
Carry out the development of new energy power generation technology, new energy power generation grid-connection operation is got more and more extensive concerning of people.Wherein wind energy and
Solar power generation is two kinds most promising in generation of electricity by new energy.Balance of electric power and ener calculating be electrical reticulation design planning with
One of the important computations of dispatching of power netwoks operation.It is the key decision foundation for realizing power science scheduling.But generation of electricity by new energy has
The uncertain of the characteristics of randomness and fluctuation, new energy available capacity and power output size brings to balance of electric power and ener calculating
Many challenges.The complexity and uncertain problem that large-scale grid connection C/B length phase balance of electric power and ener faces significantly increase.It is right
The stable operation of system brings detrimental effect.
Domestic and foreign scholars have done many researchs to the balance of electric power and ener problem of new-energy grid-connected, most of research be
In the case that generation of electricity by new energy ratio is not high, the consumption problem of new-energy grid-connected is realized by the adjusting of conventional power unit, it is final real
Existing balance of electric power and ener.But these methods by power system security constraints, new energy randomness and fluctuation feature restriction and
It causes excessive abandonment, abandon optical phenomenon.Rarely have influence of the research high proportion new-energy grid-connected to grid power electric quantity balancing, foundation is examined
The power system simulation model for considering new energy stochastic behaviour and power constraint studies the power balance and electric quantity balancing point of fining
Analysis method.
Summary of the invention
It is an object of the invention to overcome the disadvantages mentioned above of the prior art, a kind of a high proportion of new-energy grid-connected operation is proposed
Balance of electric power and ener method.
The technical solution adopted by the invention is as follows:
The characteristics such as randomness and uncontrollability first against new energy establish new energy power output model.It is commented based on reliability
The extensive new energy volume metering estimated, honourable unit can be considered that the capacity of conventional power unit is big under the premise of the reliabilities such as calculating
It is small.Using particle swarm algorithm is used, considered in power system security constraints and the smallest situation of system cost of electricity-generating at the same time, is obtained each
The power output scheme of new energy unit.
New energy of the present invention refers to wind-power electricity generation and photovoltaic power generation.
Specific step is as follows:
1, consider the random feature of new energy, establish new energy power output model；
2, using the reliability for passing through the calculating new energy access of sequence Monte Carlo method.And use electric power deficiency frequency for reliability
Benchmark cuts the searching method calculating new energy volume metering that string method is volume metering；
3, finally with the minimum objective function of system cost of electricity-generating, using particle swarm algorithm, consider that power constraint obtains respectively
The power output scheme of unit.
Each step is specific as follows:
1. considering the random feature of new energy, new energy power output model is established.
(1) relation derivation based on Wind turbines active power of output and wind speed goes out Wind turbines power output model:
In formula, pwIt contributes for Wind turbines,For the rated power of blower, vinFor the incision wind speed of blower, voutFor blower
Cut-out wind speed, vrFor the rated wind speed of blower,For the rated power of blower, v is air speed data；
When wind speed is higher than the incision wind speed v of blowerinWhen, blower starting is incorporated into the power networks；When wind speed is equal to or more than blower
Rated wind speed vrWhen, blower keeps rated power output；When wind speed is lower than the incision wind speed v of blowerinOr cutting out higher than blower
Wind speed voutWhen, fan parking and and grid disconnection；
(2) the output power p based on solar batterywGo out the power output model of photovoltaic unit with the relation derivation of irradiation level.
pw=EA η (2)
In formula, E indicates irradiation level, and A is the photovoltaic array gross area, and η is the specified photoelectric conversion efficiency of photovoltaic array.
2. calculating the reliability of new energy access using sequence Monte Carlo method is passed through, and use electric power deficiency frequency for reliability
Benchmark cuts string method as volume metering searching method calculating new energy volume metering.
It is as follows to pass through sequence Monte Carlo method formula:
In formula, T1Indicate that new energy unit operates normally duration, T2Indicate fault correction time.X1, X2It is uniformly to divide
The random number of cloth.TMTTFRepresent average new energy unit time between failures, TMTTRRepresenting fault mean repair time.λ is indicated
Failure rate, refers to the probability to break down within the unit time, μ indicates repair rate, and referring to can be repaired within the unit time
Probability.
Conventional power unit and new energy source machine component are normal operating condition and malfunction, respectively with the working time and when repairing
Between duration of two states described, it is believed that the duration of both states obeys exponential distribution.Using formula (2)
Formula and formula (3) are calculated, operation shape of each generating set in certain simulated time in available electricity generation system
State sequence.Then Load flow calculation is carried out, judges whether electricity generation system has off-the-line situation and electricity shortage event, it is final to calculate
The reliability index needed.
The electric power deficiency frequency refers in a period of time that electric system becomes electric power deficiency shape from the abundant state of electric power
The average time of state, unit are times/year.
In formula, NiRepresent the number for having cutting load state, FEFLCIndicate electric power deficiency frequency, T indicates the cutting load time.
It is to wait reliability criterions with electric power deficiency frequency, determines that original power system reliability is horizontal, wind-powered electricity generation volume metering
It is and to be soundd out repeatedly determining by adding virtual robot arm capacity into the equivalent system without new energy and conventional power unit.It asks
Solution method is to cut string method.
Reliability criterion refers in planning and designing or operation to be reliable required by reaching power generation and electrical power trans mission/distribution system
Spend be must satisfy index, condition or regulation.
Cut string method:
Cut string method formula:
In formula, f (xk) about xkFunction, k is positive integer.Calculating needs to give initial x0, x1。
3., using particle swarm algorithm, considering that power constraint obtains respectively finally with the minimum objective function of system cost of electricity-generating
The power output scheme of unit.
PSO Algorithm process is as follows:
Establish objective function:
MinF=FGt(PGt)+FWt(PWt)+FPt(PPt)+FCt (7)
In formula, min F is the objective function for taking total generation cost as the smallest economic load dispatching, and wherein F is total power production cost；
PGtIndicate conventional power unit power output size；PWtIndicate Wind turbines power output size；PPtIndicate photovoltaic unit output size；FGt(PGt)
Expression and PGtRelated conventional power unit cost of electricity-generating；FWt(PWt) indicate and PWtRelated Wind turbines cost of electricity-generating；FPt(PPt)
Expression and PPtRelated photovoltaic s unit generation cost；FCtFor penalty factor.
Power constraint condition:
ΔPi≤Pi*li (11)
Formula (8) is system power Constraints of Equilibrium, in formula: PiIndicate the power generating value of the conventional power unit in the unit time,For
I-th unit plan power output, LloadIndicate workload demand, LlossIndicate transmission losses；
Formula (9) is the constraint of unit output upper and lower bound,Respectively indicate the lower limit value of i-th unit output
With upper limit value；
Formula (10) is that node voltage amplitude bound constrains, in formula: UiFor the voltage of j-th of node,Respectively
For node j voltage magnitude upper and lower limit.
Formula (11) is unit ramp loss: Δ PiFor the changed power of i-th unit, PiFor i-th unit capacity, liFor
I-th unit climbing rate.
Particle swarm algorithm formula:
V []=ω * V []+c1*rand () * (pbest []-present [])+c2*rand () * (gbest []-
present[]) (12)
Present []=present []+V [] (13)
V [] indicates particle rapidity in formula, and ω is inertia weight, and present [] is the position of current particle, and pbest [] is
Current individual optimal solution vector, gbest [] are current global optimum's solution vectors, and rand () is the random number between [0,1], c1
It is Studying factors with c2, usually takes 2.
Particle swarm algorithm calculates step:
Step 1: initialization a group particle present [], each particle be three-dimensional vector, respectively represent conventional power unit and
Wind turbines and photovoltaic unit output size.
Step 2: judging power system security constraints, meets power system security constraints, then objective function is F in formula (7)CtIt is 0.No
Meet power system security constraints, then objective function is F in formula (7)CtFor an infinitely great number.Particle is substituted into objective function and is asked
Obtain pbest [] and gbest [].
Step 3: new present [] is found out by formula (12) and formula (13).
Step 4: judging that the current global optimum's solution vector gbest [] of termination condition is less than allows required precision, and satisfaction terminates
Condition, which then calculates, to be terminated, and is unsatisfactory for, and step 2 is gone to.
Detailed description of the invention
String method schematic diagram is cut in capacity search that Fig. 1 wind-powered electricity generation is credible；
The flow chart of Fig. 2 balance of electric power and ener method of the present invention.
Specific embodiment
The present invention is further illustrated below in conjunction with the drawings and the specific embodiments.
As shown in Fig. 2, the process that the present invention refines balance of electric power and ener method is as follows:
1, consider that the random feature of new energy, the relationship based on new energy environmental data and output power establish new energy power output
Model；
2, the appearance for asking volume metering the conventional power unit that honourable unit under the premise of reliabilities can be considered as such as to calculate is utilized
Measure size.Volume metering considers the Calculation of Reliability of new energy access using sequence Monte Carlo method is passed through, and insufficient using electric power
Frequency is reliability benchmark, a section string method is volume metering searching method.
It cuts string method and realizes that volume metering search calculates as shown in Figure 1.
Horizontal axis is the installed capacity of virtual robot arm in the equivalent system of electric system in figure, and the longitudinal axis is equivalent system reliability
Index, reliability index numerical value is bigger, and expression equivalent system reliability is lower.C(Pv) it is equivalent system reliability with virtual robot arm
Capacity PvChange curve, R0For original system reliability level, wind-powered electricity generation is credible capacity, that is, C (Pv) and R0Intersection point.String method is cut to calculate
In, the corresponding Reliability Index of bound of virtual robot arm capacity is calculated first, virtual robot arm lower bound of capacity is set as 0, on
Limit PmaxIt can be set as wind power plant capacity.The line L between two o'clock is being in figure1, calculate L1With R0The corresponding horizontal axis coordinate P of intersection point1,
And then calculating virtual robot arm capacity is P1When equivalent system reliability index, and then obtain L2.L is calculated again2With P0Intersection point is corresponding
Horizontal axis coordinate P2, and so on iteration, respectively obtains P3,P4,P5... until convergence.
3, finally with the minimum objective function of system cost of electricity-generating, using particle swarm algorithm, consider that power constraint obtains respectively
The power output scheme of unit.Realize the power balance and electric quantity balancing of fining.
Claims (4)
1. a kind of balance of electric power and ener method of new-energy grid-connected operation, which is characterized in that the balance of electric power and ener method
The step of are as follows: the characteristics such as randomness and uncontrollability first against new energy establish new energy power output model；Based on reliability
The extensive new energy volume metering of assessment, the honourable unit under the premise of reliabilities such as calculating can be considered the capacity of conventional power unit
Size；Using particle swarm algorithm is used, is considered in power system security constraints and the smallest situation of system cost of electricity-generating at the same time, obtained
The power output scheme of each new energy unit；The new energy refers to wind-power electricity generation and photovoltaic power generation.
2. balance of electric power and ener method according to claim 1, which is characterized in that in the step (1), consider new energy
Random feature establishes new energy power output model:
(1) relation derivation based on Wind turbines active power of output and wind speed goes out Wind turbines power output model:
In formula, pwIt contributes for Wind turbines,For the rated power of blower, vinFor the incision wind speed of blower, voutFor cutting for blower
Wind speed out, vrFor the rated wind speed of blower,For the rated power of blower, v is air speed data；
When wind speed is higher than the incision wind speed v of blowerinWhen, blower starting is incorporated into the power networks；When wind speed is equal to or more than the specified of blower
Wind speed vrWhen, blower keeps rated power output；When wind speed is lower than the incision wind speed v of blowerinOr the cut-out wind speed higher than blower
voutWhen, fan parking and and grid disconnection；
(2) the output power p based on solar batterywGo out the power output model of photovoltaic unit with the relation derivation of irradiation level；
pw=EA η (2)
In formula, E indicates irradiation level, and A is the photovoltaic array gross area, and η is the specified photoelectric conversion efficiency of photovoltaic array.
3. balance of electric power and ener method according to claim 1, which is characterized in that calculated newly using sequence Monte Carlo method is passed through
The reliability of energy access, and use electric power deficiency frequency for reliability benchmark, a section string method is volume metering searching method meter
Calculate new energy volume metering；
It is as follows to pass through sequence Monte Carlo method formula:
In formula, T1Indicate that new energy unit operates normally duration, T2Indicate fault correction time；X1, X2It is equally distributed
Random number；TMTTFRepresent average new energy unit time between failures, TMTTRRepresenting fault mean repair time；λ indicates failure
Rate refers to that the probability to break down within the unit time, μ indicate repair rate, refer to the probability that can be repaired within the unit time；
Conventional power unit and new energy source machine component are normal operating condition and malfunction, respectively with working time and repair time come
Described, it is believed that the duration of both states obeys exponential distribution the duration of two states；Using formula (2) formula
Operating status sequence of each generating set in certain simulated time in electricity generation system is calculated with formula (3)；Then into
Row Load flow calculation, judges whether electricity generation system has off-the-line situation and electricity shortage event, and be finally calculated needs can
By property index；
The electric power deficiency frequency refers in a period of time that electric system becomes electric power deficiency state from the abundant state of electric power
Average time, unit are times/year；
In formula, NiRepresent the number for having cutting load state, FEFLCIndicate electric power deficiency frequency, T indicates the cutting load time；
It is to wait reliability criterions with electric power deficiency frequency, determines that original power system reliability is horizontal, wind-powered electricity generation volume metering is logical
It crosses and adds virtual robot arm capacity into the equivalent system without new energy and conventional power unit, and sound out determination repeatedly.Solution side
Method is to cut string method；
Reliability criterion refers to the reliability institute in planning and designing or operation to make power generation and electrical power trans mission/distribution system reach required
Index, condition or the regulation that must satisfy；
Cut string method formula are as follows:
In formula, f (xk) about xkFunction, k is positive integer.Calculating needs to give initial x0, x1。
4. balance of electric power and ener method according to claim 1, which is characterized in that step (3) particle swarm algorithm is asked
Solution preocess is as follows:
Establish objective function:
MinF=FGt(PGt)+FWt(PWt)+FPt(PPt)+FCt (7)
In formula, min F is the objective function for taking total generation cost as the smallest economic load dispatching, and wherein F is total power production cost；PGtTable
Show conventional power unit power output size；PWtIndicate Wind turbines power output size；PPtIndicate photovoltaic unit output size；FGt(PGt) indicate
With PGtRelated conventional power unit cost of electricity-generating；FWt(PWt) indicate and PWtRelated Wind turbines cost of electricity-generating；FPt(PPt) indicate
With PPtRelated photovoltaic s unit generation cost；FCtFor penalty factor；
Power constraint condition:
ΔPi≤Pi*li (11)
Formula (8) is system power Constraints of Equilibrium, in formula: PiIndicate the power generating value of the conventional power unit in the unit time, Pi WIt is i-th
Unit plan power output, LloadIndicate workload demand, LlossIndicate transmission losses；
Formula (9) is the constraint of unit output upper and lower bound, Pi max、Pi minRespectively indicate the lower limit value and the upper limit of i-th unit output
Value；
Formula (10) is that node voltage amplitude bound constrains, in formula: UiFor the voltage of j-th of node,Respectively save
The upper limit of point j voltage magnitude, lower limit；
Formula (11) is unit ramp loss: Δ PiFor the changed power of i-th unit, PiFor i-th unit capacity, liIt is i-th
Unit climbing rate；
Particle swarm algorithm formula:
V []=ω * V []+c1*rand () * (pbest []-present [])+c2*rand () * (gbest []-present
[]) (12)
Present []=present []+V [] (13)
V [] indicates particle rapidity in formula, and ω is inertia weight, and present [] is the position of current particle, and pbest [] is current
Individual optimal solution vector, gbest [] are current global optimum's solution vectors, and rand () is the random number between [0,1], c1 and c2
It is that Studying factors usually take 2；
Particle swarm algorithm calculates step:
Step 1: initialization a group particle present [], each particle are three-dimensional vector, respectively represent conventional power unit and scene
Unit output size；
Step 2: judging power system security constraints, meets power system security constraints, then objective function is F in formula (7)CtIt is 0, is unsatisfactory for
Power system security constraints, then objective function is F in formula (7)CtFor an infinitely great number；Particle is substituted into objective function and is acquired
Pbest [] and gbest []；
Step 3: new present [] is found out by formula (12) and formula (13)；
Step 4: judging that the current global optimum's solution vector gbest [] of termination condition is less than allows required precision, meets termination condition
Then calculating terminates, and is unsatisfactory for, goes to step 2.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110648249A (en) * | 2019-09-23 | 2020-01-03 | 广西电网有限责任公司 | Annual power balance measuring and calculating method, device and equipment |
| CN111987740A (en) * | 2020-07-21 | 2020-11-24 | 浙江中新电力工程建设有限公司 | Electric power and electric quantity balancing method for new energy grid-connected operation |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101710711A (en) * | 2009-12-21 | 2010-05-19 | 中国电力科学研究院 | Monitoring system for energy storing device for output power of smooth wind power system |
| CN102280878A (en) * | 2011-07-26 | 2011-12-14 | 国电南瑞科技股份有限公司 | Wind power penetration optimization evaluation method based on SCED |
| CN102436631A (en) * | 2012-01-18 | 2012-05-02 | 重庆大学 | Method for evaluating reliability of wind/diesel/ storage hybrid system |
| CN104037790A (en) * | 2014-05-22 | 2014-09-10 | 南方电网科学研究院有限责任公司 | Evaluation method of acceptance capability of new energy based on sequential Monte Carlo simulation |
| CN104362673A (en) * | 2014-10-29 | 2015-02-18 | 国网甘肃省电力公司 | Wind power integration coordinated dispatching optimization method based on peak regulation margin |
| CN105024398A (en) * | 2015-06-29 | 2015-11-04 | 国电南瑞科技股份有限公司 | Optimization scheduling method based on optimal wind power confidence |
| CN107276070A (en) * | 2017-06-12 | 2017-10-20 | 重庆大学 | The generating and transmitting system operational reliability modeling of meter and a frequency modulation frequency modulation and its appraisal procedure |
| CN108879657A (en) * | 2018-06-13 | 2018-11-23 | 湖北省电力勘测设计院有限公司 | Balance of electric power and ener optimization method based on wind-powered electricity generation volume metering |
| CN109066807A (en) * | 2018-08-03 | 2018-12-21 | 国网新疆电力有限公司电力科学研究院 | The fiery bundling of scene containing energy storage sends power source planning method outside |
-
2019
- 2019-01-25 CN CN201910074108.8A patent/CN109713720B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101710711A (en) * | 2009-12-21 | 2010-05-19 | 中国电力科学研究院 | Monitoring system for energy storing device for output power of smooth wind power system |
| CN102280878A (en) * | 2011-07-26 | 2011-12-14 | 国电南瑞科技股份有限公司 | Wind power penetration optimization evaluation method based on SCED |
| CN102436631A (en) * | 2012-01-18 | 2012-05-02 | 重庆大学 | Method for evaluating reliability of wind/diesel/ storage hybrid system |
| CN104037790A (en) * | 2014-05-22 | 2014-09-10 | 南方电网科学研究院有限责任公司 | Evaluation method of acceptance capability of new energy based on sequential Monte Carlo simulation |
| CN104362673A (en) * | 2014-10-29 | 2015-02-18 | 国网甘肃省电力公司 | Wind power integration coordinated dispatching optimization method based on peak regulation margin |
| CN105024398A (en) * | 2015-06-29 | 2015-11-04 | 国电南瑞科技股份有限公司 | Optimization scheduling method based on optimal wind power confidence |
| CN107276070A (en) * | 2017-06-12 | 2017-10-20 | 重庆大学 | The generating and transmitting system operational reliability modeling of meter and a frequency modulation frequency modulation and its appraisal procedure |
| CN108879657A (en) * | 2018-06-13 | 2018-11-23 | 湖北省电力勘测设计院有限公司 | Balance of electric power and ener optimization method based on wind-powered electricity generation volume metering |
| CN109066807A (en) * | 2018-08-03 | 2018-12-21 | 国网新疆电力有限公司电力科学研究院 | The fiery bundling of scene containing energy storage sends power source planning method outside |
Non-Patent Citations (2)
| Title |
|---|
| 张硕: "计及风电场容量可信度的电力系统可靠性研究", 《万方学位论文全文数据库》 * |
| 张硕: "计及风电场容量可信度的电力系统可靠性研究", 《万方学位论文全文数据库》, 15 February 2011 (2011-02-15), pages 26 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110648249A (en) * | 2019-09-23 | 2020-01-03 | 广西电网有限责任公司 | Annual power balance measuring and calculating method, device and equipment |
| CN110648249B (en) * | 2019-09-23 | 2022-03-29 | 广西电网有限责任公司 | Annual power balance measuring and calculating method, device and equipment |
| CN111987740A (en) * | 2020-07-21 | 2020-11-24 | 浙江中新电力工程建设有限公司 | Electric power and electric quantity balancing method for new energy grid-connected operation |
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