CN104779611B - Micro-capacitance sensor economic load dispatching method based on centralized and distributed dual-layer optimization strategy - Google Patents
Micro-capacitance sensor economic load dispatching method based on centralized and distributed dual-layer optimization strategy Download PDFInfo
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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
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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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
- 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
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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
- 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
- Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
- Y04S10/123—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving renewable energy sources
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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
- 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
Abstract
The invention discloses the micro-capacitance sensor economic load dispatching method based on centralized and distributed dual-layer optimization strategy, this method is under micro-capacitance sensor isolated power grid state, using economy as the Optimization Scheduling of target.This method in the dual-layer optimization of integrated distribution formula, concentrated layer correspondence exerted oneself based on each uncontrollable unit in micro-capacitance sensor and load prediction data and the advanced optimization process an of predetermined period that carries out, the process of this method completes by micro-capacitance sensor centralized-control center；Distributed optimization correspondence exerted oneself based on each uncontrollable unit of each scheduling instance micro-capacitance sensor in predetermined period length and micro-grid load real time data and the real-time optimization procedure that carries out, the optimization process is distributed progress, is completed by the micro source controller being built into micro-capacitance sensor each unit.The present invention is proposed during not considering that the economy of energy storage, i.e. energy storage are not involved in economy optimization.
Description
Technical field
The present invention relates to a kind of micro-capacitance sensor economic load dispatching method based on centralized and distributed dual-layer optimization strategy, belong to
Micro-capacitance sensor technical field.
Background technology
As power network scale increases rapidly, because traditional power network energy resource consumption is big, environmental pollution is serious, and via net loss is high,
It is increasingly difficult to meet the requirement of user's high reliability and diversified power supply strategy.Distributed power generation has energy utilization rate
It is high, environmental pollution is small, reliability is high, convenient and flexible installation and many advantages, such as inexpensive high repayment, can efficiently solve
The many potential problems in traditional power network ground.Distributed power source is mainly included by the internal combustion engine of fuel of gas or liquid, fuel electricity
Pond, miniature gas turbine, regenerative resource, such as：The distributed electrical source position of wind energy or solar power generation is scattered flexible, low-carbon ring
The features such as guarantor, has greatly adapted to scattered resource and electricity needs, has delayed to cause electrical power trans mission/distribution system due to the increase of load
Huge investment required for safeguarding, reduces the cost depletions brought due to long-distance transmissions electric energy.In addition, it is distributed
Power supply and traditional power network complement one another, and drastically increase the reliability of power supply.
Microgrid can cause distributed power source flexibly, efficiently to run, and fully excavate the value and benefit of distributed power generation.
Microgrid scale can couple buffering distributed power generation and bulk power grid between distributed power generation and bulk power grid, can also be independent
Operation.It is the advanced form of distributed power source development.Microgrid problem from the point of view of systematic perspective, by generator, load, energy storage device
And control device etc. is combined, a small-sized controllable hair electrical power trans mission/distribution system is formed.Microgrid has certain energy management capabilities, passes through
Microgrid access distributed power source turns into preferable selection.DG in microgrid can be divided into intermittent power supply and company by characteristics of output power
The continuous class of property power supply two, intermittent power supply includes wind-power electricity generation and photovoltaic generation, shadow of its power output by natural conditions such as weather
Sound is larger, with obvious fluctuation and uncertainty, and continuity power supply includes miniature gas turbine and fuel cell etc., and it has
There are relatively reliable primary energy supply and continuous processing regulating power.
Current power system scale is being continuously increased with complexity, and the data explosion that Power System Interconnection is brought increases, with
And the network coordination problem that generation of electricity by new energy is brought is introduced, all it is the huge challenge to conventional electric power network analysis.And it is of the invention
The problem of can solving above well.
The content of the invention
Present invention aims at there is provided a kind of micro-capacitance sensor economy tune based on centralized and distributed dual-layer optimization strategy
Degree method, this method is applied to distributed scheduling mode in microgrid energy optimum management, with reference to traditional centralized scheduling side
Formula, forms double-deck centralized and distributed energy-optimised management strategy.The distributed method can transfer all scheduled pairs
As participating in scheduling calculating task, calculating task decentralized processing is realized, the computing resource of unit in micro-capacitance sensor is taken full advantage of.
The present invention solves the technical method taken of its technical problem：One kind is based on centralized and distributed dual-layer optimization
Micro-capacitance sensor economic load dispatching optimization process is divided into the concentration based on prediction data by the micro-capacitance sensor economic load dispatching method of strategy, this method
Formula dispatch layer and the distributed scheduling layer based on real time data, specifically include following steps：
Step 1：The uncontrollable micro battery of micro-capacitance sensor is pre- in following predetermined period of control centre's collection of micro-capacitance sensor
Measure force data, such as wind-powered electricity generation and photovoltaic generation unit are exerted oneself, and the total load prediction data of micro-capacitance sensor.
Step 2：Micro-capacitance sensor control centre is according to information of forecasting, using economic optimum as target, is considering each constraints
Under the premise of, calculated using particle group optimizing method through row optimization, the distributed power source for obtaining whole predetermined period is exerted oneself.Real-time
Each DG units are exerted oneself by prediction before scheduling instance arrives is produced.Its object function is：
Min f=fDG+fL
Wherein f represents cost, and U represents state, and synchronization can only take 0 or 1.P represents power.DG represents controllable micro- electricity
Source, N represents the when hop count divided to a whole predetermined period, and L represents cut-off load, and Q represents element number, and K represents micro- electricity
The maintenance cost in source, c represents price, and on represents the start and stop of micro battery, and * represents the change of micro battery state, and F represents micro battery
Cost of electricity-generating function.Remember that optimum results areWithThe plan of respectively each controllable micro battery is exerted oneself and predicted
Cutting load state.
The constraints that centralized optimization process is followed is as follows：
(1) power-balance constraint
Unctrl represents uncontrollable micro battery.
(2) state constraint
The formula represents repeatedly cut off a certain load within continuous m scheduling slot.
(3) DG units limits
The exert oneself upper limit and the lower limit of respectively each DG units.Represent the maximum climbing rate of each unit.
Step 3：When the new Real-Time Scheduling moment arrives, centralized-control center detect uncontrollable power supply exert oneself in real time and
The real time data of load.According to specifically network service topological diagram, the start node of distributed optimization is determined.According to real time data
The error of the scheduling instance is calculated with prediction data.And the amount of error is added in the prediction power generating value of start node.Error
ΔPtCalculation formula be：
It is real time data with target on realtime.
After selected optimization start node, its incoming prediction of error is exerted oneself, calculation formula is：
, will before now starting to the execution of step 5 micro battery to actRegard a global variable as, optimized
It can be changed in journey by the optimization process of each node, but before the process concludes, each DG, which is not adjusted, to exert oneself.
Step 4：Since start node, the communication connection figure of controllable micro battery composition each to micro-capacitance sensor is suitable by a certain traversal
Sequence is traveled through.Note t micro-capacitance sensor communication topology non-directed graph adjacency matrix beTake 1 expression i and j
Between have syntople, take 0 not have.And if i=j,Micro-capacitance sensor centralized-control center controls the node time of the whole network
Go through process.Begun stepping through from start node, the node traversed carries out 1 suboptimization calculating, i.e., the optimization that each node is carried out is calculated
It is not parallel, but follows the sequencing of traversal through row.Its object function is：
The constraints of satisfaction is：
(1) power-balance constraint：
(2) DG units limits
Just optimum results are substituted after being calculated once per node originalAnd node adjacent thereto
After complete net node is traveled through, convergence judgement is carried out, repeat step 4 if not converged, convergence then goes to step 5.
Step 5：Each controllable micro battery is exerted oneself according to the adjustment of distributed real-time optimization result.Judge whether a prediction week
Phase terminates, if not terminating, when waiting next scheduling instance arrival, and goes to step 3.Otherwise terminate.
The above method of the present invention is the centralized optimization layer exerted oneself based on uncontrollable micro battery with the prediction data of load.
The above method of the present invention is the distributed optimization layer exerted oneself based on uncontrollable micro battery with the real time data of load.
Beneficial effect：
1st, various prediction data of the concentrated layer based on micro-capacitance sensor of the invention, can to controllable micro battery through row pre-scheduling,
According to reliable prediction data, and the scheduling result that system is respectively constrained and drawn is met, its result data has reliability.
2nd, distributed optimization of the invention is based on real time data and in the case where meeting each constraint, can be to forecast dispatching
As a result it is adjusted, uncertain demand in real time can be met, make micro-capacitance sensor safe and reliable operation, its distributed optimization meter
Calculation process so that calculating task is dispersed at each scheduling node, the burden of reduction micro-capacitance sensor control centre, and be easy to implement, have
There is feasibility.
3rd, the present invention can be applied to the isolated power grid state of the micro-capacitance sensor of various scales, response speed in force
It hurry up.
4th, Distributed Calculation of the present invention can utilize same of the computing resource parallel processing of multiple variety classes loose couplings
The calculation of business, is calculated, it is cheaper, and make full use of calculating to provide compared to the centralization carried out using supercomputer
Source.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention.
Fig. 2 is the micro-capacitance sensor structural representation of the embodiment of the present invention.
Fig. 3 is the Communication topology schematic diagram of the micro-capacitance sensor of the embodiment of the present invention.
Embodiment
The invention is described in further detail with reference to Figure of description.
As shown in Fig. 2 the present invention micro-capacitance sensor be by a typhoon power generator (i.e.：WT), photovoltaic generation unit
(i.e.：PV), two miniature gas turbines are (i.e.：MT), two diesel-driven generators are (i.e.：DE), one group of fuel cell is (i.e.：FC), one
Group energy-storage units are (i.e.：) and the part such as household loads is constituted Bat.
As shown in figure 1, a kind of micro-capacitance sensor economic load dispatching method based on centralized and distributed dual-layer optimization strategy, the party
Micro-capacitance sensor economic load dispatching optimization process is divided into the centralized scheduling layer based on prediction data and the distribution based on real time data by method
Formula dispatch layer, specifically includes following steps：
Step 1：The uncontrollable micro battery of micro-capacitance sensor is pre- in following predetermined period of control centre's collection of micro-capacitance sensor
Measure force data, and total load.Predetermined period that example is used, for 24 hours, was a scheduling instance every 1 hour.Allusion quotation
The predicted value of the micro-capacitance sensor family load of type is as shown in table 1.Wind-power electricity generation (WT) and photovoltaic generation (PV) are exerted oneself predicted value such as table 2
It is shown.
Table 1
Table 2
Step 2：Information of forecasting of the micro-capacitance sensor control centre according to obtained by step 1, using economic optimum as target, is considering each
On the premise of constraints, calculated through row optimization, the distributed power source for obtaining whole predetermined period is exerted oneself.At the Real-Time Scheduling moment
Each DG units are exerted oneself by prediction before arrival is produced.
The constraint of each generator unit is as shown in table 3.
Table 3
By the calculating of centralized optimization, draw five controllable distributed power generation units (be expressed as MT1, MT2, FC,
DE1 and DE2) 24 hours prediction exert oneself as shown in table 4.
Table 4
Step 3：Micro-capacitance sensor Communication topology in embodiment is as shown in figure 3, and it is distributed optimization starting section to determine MT1
Point.When the new Real-Time Scheduling moment arrives, centralized-control center gather uncontrollable power supply exert oneself in real time and load real-time number
According to.
Exemplified by when the 0th, prediction data, real time data and error are as shown in table 5
Table 5
On the prediction that the margin of error is added into MT1 is exerted oneself, therefore MT1 prediction is exerted oneself and is changed into 29.9645kW when 0, and uses it
Instead of predicting corresponding item in table of exerting oneself.But before step 5 execution, all without the actual adjustment exerted oneself.Other scheduling
The process at moment is by that analogy.
Step 4：Can obtain adjacency matrix according to Fig. 3 micro-capacitance sensor communication topology figure is：
Assuming that carrying out distributed optimization according to 1-2-3-4-5 traversal order since node 1 (MT1).In 1 node when 0
On the object function of optimization be：
And meet power-balance constraint：
Here the information in a table being made up of global variable, its meeting are can be regarded as with target amount on forecast
Changed by the optimum results of distributed optimization process each time, such as 1 node at 0 is completed after optimization, can be usedRespectively instead of in tableProcess on other nodes is with this
Analogize.
All nodes in the whole network are traveled through and completed after optimization calculating, are carried out criteria for convergence, that is, are judged all nodes
The knots modification being worth before and after 1 suboptimization process of exerting oneself absolute value whether be less than a certain set-point.It is considered as convergence if meeting
And 5 are gone to step, otherwise repeat step 4, until result convergence.
Step 5：Each controllable micro battery is exerted oneself according to the adjustment of distributed real-time optimization result.Exemplified by when the 0th, through undue
After cloth optimization, the result such as table 6 of exerting oneself of each controllable micro battery is drawn.
Table 6
| DG nodes | MT1 | DE1 | FC | MT2 | DE2 |
| Exert oneself (kW) | 24.5330 | 0 | 22.9334 | 24.5330 | 0 |
Judge whether that a predetermined period terminates, if not terminating, when waiting next scheduling instance arrival, and go to step 3.
Otherwise terminate.
Finally, the distributed optimization result of 24 hours is given in table 7.
Table 7
Claims (6)
1. the micro-capacitance sensor economic load dispatching method based on centralized and distributed dual-layer optimization strategy, it is characterised in that methods described
Comprise the following steps：
Step 1：The micro-capacitance sensor prediction data in following predetermined period is collected by the control centre of micro-capacitance sensor, including uncontrollable micro-
Power supply predicts force data, i.e.,：Wind-powered electricity generation and photovoltaic generation unit are exerted oneself, and the total load data of micro-capacitance sensor；
Step 2：Micro-capacitance sensor control centre is according to information of forecasting, using economic optimum as target, is considering the premise of each constraints
Under, calculating is optimized using particle group optimizing method, the distributed power source for obtaining whole predetermined period is exerted oneself, in Real-Time Scheduling
Each controllable micro battery DG units are exerted oneself by prediction before moment arrives is produced；
Step 3：When the new Real-Time Scheduling moment arrives, centralized-control center detects exerting oneself and load in real time for uncontrollable power supply
Real time data, according to specific network service topological diagram, determine the start node of distributed optimization, according to real time data with it is pre-
Survey data and calculate the error of the scheduling instance, and in the prediction power generating value of start node addition error amount；
Step 4：Since start node, the communication connection figure of controllable micro battery composition each to micro-capacitance sensor is entered by a certain traversal order
Row traversal, often traverses a node, and the node just carries out primary particle group's optimization under constraints and calculated, and optimization aim is
Itself node adjacent thereto is exerted oneself, and is exerted oneself using optimum results instead of prediction, after complete net node is traveled through, is restrained
Property judge, the repeat step 4 if not converged, convergence then go to step 5；
Step 5：Each controllable micro battery is exerted oneself according to the adjustment of distributed real-time optimization result, judges whether predetermined period knot
Beam, if not terminating, when waiting the next scheduling instance to arrive, and goes to step 3, otherwise terminates.
2. a kind of micro-capacitance sensor economic load dispatching side based on centralized and distributed dual-layer optimization strategy according to claim 1
Method, it is characterised in that：The centralized optimization that methods described is exerted oneself with the prediction data of load based on uncontrollable micro battery.
3. a kind of micro-capacitance sensor economic load dispatching side based on centralized and distributed dual-layer optimization strategy according to claim 1
Method, it is characterised in that：The distributed optimization that methods described is exerted oneself with the real time data of load based on uncontrollable micro battery.
4. a kind of micro-capacitance sensor economic load dispatching side based on centralized and distributed dual-layer optimization strategy according to claim 1
Method, it is characterised in that the object function of centralized optimization is in the step 2 of methods described：
Minf=fDG+fL
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5. a kind of micro-capacitance sensor economic load dispatching side based on centralized and distributed dual-layer optimization strategy according to claim 1
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, will before now starting to the execution of step 5 micro battery to actRegarding as can in a global variable, optimization process
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6. the micro-capacitance sensor economic load dispatching method according to claim 1 based on centralized and distributed dual-layer optimization strategy,
It is characterized in that：Methods described be by micro-capacitance sensor economic load dispatching optimization process be divided into based on prediction data centralized scheduling layer and
Distributed scheduling layer based on real time data.
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| CN105281372B (en) * | 2015-10-09 | 2016-08-24 | 南京邮电大学 | The multiple target multiagent distributed game optimization method of the Based on Distributed energy |
| CN105406520B (en) * | 2016-01-06 | 2017-12-29 | 重庆邮电大学 | Independent micro-capacitance sensor economic load dispatching optimization method based on dual master control dynamic cooperative |
| CN105610198B (en) * | 2016-01-20 | 2017-11-17 | 南京邮电大学 | Power system static economic load dispatching method based on colony's experience artificial bee colony algorithm |
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| CN110690719B (en) * | 2019-09-18 | 2021-03-30 | 国网重庆市电力公司电力科学研究院 | Micro-grid battery energy storage configuration method and readable storage medium |
| CN111509718A (en) * | 2020-05-31 | 2020-08-07 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Safety and stability control system and method for power transmission and transformation |
| CN112234608A (en) * | 2020-09-25 | 2021-01-15 | 国能日新科技股份有限公司 | Real-time library active power control system based on combination of centralized type and distributed type |
| CN113054685B (en) * | 2021-04-15 | 2023-01-31 | 淮阴工学院 | Solar micro-grid scheduling method based on crow algorithm and pattern search algorithm |
| CN114039354B (en) * | 2021-10-11 | 2023-05-30 | 南京邮电大学 | Multi-micro-grid fully-distributed secondary voltage and energy level fault-tolerant control system |
| CN116205377B (en) * | 2023-04-28 | 2023-08-18 | 江西恒能电力工程有限公司 | Distributed photovoltaic power station output prediction method, system, computer and storage medium |
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