CN110472780A - A kind of distribution Optimization Scheduling neural network based - Google Patents
A kind of distribution Optimization Scheduling neural network based Download PDFInfo
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- 230000007246 mechanism Effects 0.000 claims description 4
- 238000002922 simulated annealing Methods 0.000 claims description 4
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 3
- 230000019637 foraging behavior Effects 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
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- 238000007599 discharging Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
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- 238000005295 random walk Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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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
- 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
- Y04S10/50—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 a kind of distribution Optimization Schedulings neural network based, by determining optimization aim, continuous variable and integer variable are determined according to optimization aim, establish Optimal Operation Model, determine constraint condition, the running continuous variable data of acquisition distribution and integer variable data later, optimization is carried out to distribution Optimal Operation Model and obtains optimal Optimized Operation scheme, and pass through the continuous variable data and integer variable data after acquisition Optimized Operation again, Optimized Operation data set is trained, training Optimal Operation Model, obtain the distribution Optimal Operation Model of deep learning, optimization is carried out again obtains optimal Optimized Operation scheme, it can be according to the variation with real-time parameter in electricity operation, Optimal Operation Model is corrected in real time, make Optimal Operation Model that can more reflect the actual conditions with electricity operation , obtain more accurate Optimized Operation scheme.
Description
Technical field
The present invention relates to distribution optimisation technique fields, particularly relate to a kind of distribution Optimized Operation side neural network based
Method.
Background technique
The problems such as energy shortage, environmental pollution, drives clean energy resource generation technology to grow rapidly, the following power distribution network ecology potential
Necessarily clean reproducible energy is made full use of to generate electricity, meets all-embracing, the realization that generates electricity to a large amount of clean reproducible energies
The hypersynchronous of renewable energy power generation and consumption in full.But as the permeability of distribution type renewable energy power generation is in electric power
The method of operation of continuous improvement in each level of system, power distribution network becomes compared with the power distribution network for not accessing distributed generation resource originally
More complicated, the hypersynchronous of distribution type renewable energy power generation also produces the supervision method and economy of power distribution network very big
Influence, conventional electrical distribution net has that consumption clean reproducible energy scarce capacity, self-regulation ability is low, scheduling mode is backward etc.
Disadvantage, for this purpose, the concept of active distribution system is come into being；
The notable feature of active distribution system shows distributed generation unit, energy-storage units in access power distribution network etc. all
Be it is controllable, the distributed generation resource in active distribution system will participate in the traffic control of power distribution network, and not conventional electrical distribution system
In simple connection.This make active distribution system Optimization Scheduling be active management distributed generation resource, realize network security,
The core technology of economical operation.However, due to the fluctuation of intermittent renewable energy power generation output power, energy storage device itself
The correlation of charging and discharging state in dispatching cycle caused by energy limit, so that the Optimization Scheduling of active distribution system is very
Therefore complexity for the feature of active distribution system, studies its Optimization Scheduling and is of great significance；
But existing distribution Optimization Scheduling, can not be according to the actual conditions with electricity operation to tactful and scheme determination
It is adjusted in real time.
Summary of the invention
In view of this, being used for it is an object of the invention to propose a kind of distribution Optimization Scheduling neural network based
Solve above-mentioned the problems of the prior art.
Based on a kind of above-mentioned purpose distribution Optimization Scheduling neural network based provided by the invention, method includes:
Determine the optimization aim of distribution Optimized Operation；
Distribution Optimized Operation continuous variable, integer variable are determined according to optimization aim；
According to optimization aim, continuous variable and integer variable, distribution Optimal Operation Model is established；
Determine the constraint condition of distribution Optimal Operation Model；
The running continuous variable data of distribution, integer variable data are acquired, distribution Optimal Operation Model are carried out optimal
Change to solve and obtains optimal distribution Optimized Operation scheme；
The running continuous variable data of distribution, integer variable data after obtaining Optimized Operation again, training distribution are excellent
Change scheduling data set；
By the distribution Optimized Operation data set after training, training distribution Optimal Operation Model obtains matching for deep learning
Electrically optimized scheduling model；
Optimization is carried out to distribution Optimal Operation Model and obtains optimal distribution Optimized Operation scheme.
Preferably, when carrying out optimization to distribution Optimal Operation Model, using artificial fish-swarm algorithm.
Preferably, the Metropolis criterion in simulated annealing is introduced to the foraging behavior of artificial fish-swarm algorithm
In, artificial fish-swarm algorithm is improved.
Preferably, modelling factors are introduced into artificial fish-swarm algorithm, the local search of ASFA and SA at low temperature is had
Machine fusion.
Preferably, according to the prediction of photovoltaic power generation power output, wind power output prediction and load prediction according to probability density function and
Uncertain factor is introduced distribution Optimal Operation Model by the prediction of its Cumulative Distribution Function.
Preferably, when establishing distribution Optimal Operation Model, introducing can reduction plans and two class flexibility of transferable load it is negative
Lotus, the calculating that flexible load dispatches cost use step compensation mechanism, and excitation flexible load user participates in Demand Side Response.
From the above it can be seen that distribution Optimization Scheduling neural network based provided by the invention, by true
Determine optimization aim, continuous variable and integer variable determined according to optimization aim, establishes Optimal Operation Model, determine constraint condition,
The running continuous variable data of acquisition distribution and integer variable data later optimize to distribution Optimal Operation Model and be asked
Solution obtains optimal Optimized Operation scheme, and passes through the continuous variable data and integer variable number after acquisition Optimized Operation again
According to, Optimized Operation data set is trained, training Optimal Operation Model obtains the distribution Optimal Operation Model of deep learning,
Optimization is carried out again obtains optimal Optimized Operation scheme, it can be according to the variation with real-time parameter in electricity operation, to excellent
Change scheduling model to be corrected in real time, makes Optimal Operation Model that can more reflect the actual conditions with electricity operation, obtain subject to more
True Optimized Operation scheme.
Detailed description of the invention
Fig. 1 is the distribution Optimization Scheduling flow diagram of the embodiment of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference
Attached drawing, the present invention is described in more detail.
A kind of distribution Optimization Scheduling neural network based, comprising the following steps:
101 determine the optimization aim of distribution Optimized Operation；
102 determine distribution Optimized Operation continuous variable, integer variable according to optimization aim；
103, according to optimization aim, continuous variable and integer variable, establish distribution Optimal Operation Model；
104 determine the constraint condition of distribution Optimal Operation Model；
The running continuous variable data of 105 acquisition distribution, integer variable data, carry out most distribution Optimal Operation Model
Optimization Solution obtains optimal distribution Optimized Operation scheme；
106 again obtain Optimized Operation after the running continuous variable data of distribution, integer variable data, training distribution
Optimized Operation data set；
107 obtain deep learning by the distribution Optimized Operation data set after training, training distribution Optimal Operation Model
Distribution Optimal Operation Model；
108 pairs of distribution Optimal Operation Models carry out optimization and obtain optimal distribution Optimized Operation scheme.
Distribution Optimization Scheduling neural network based proposed by the present invention, by determining optimization aim, according to optimization
Target determines continuous variable and integer variable, establishes Optimal Operation Model, determines constraint condition, and it is running to acquire distribution later
Continuous variable data and integer variable data carry out optimization to distribution Optimal Operation Model and obtain optimal Optimized Operation side
Case, and by again obtain Optimized Operation after continuous variable data and integer variable data, to Optimized Operation data set into
Row training, training Optimal Operation Model, obtains the distribution Optimal Operation Model of deep learning, carries out optimization again and obtains
Optimal Optimized Operation scheme can repair Optimal Operation Model according to the variation with real-time parameter in electricity operation in real time
Just, make Optimal Operation Model that can more reflect the actual conditions with electricity operation, obtain more accurate Optimized Operation scheme.
In an embodiment of the present invention, it when carrying out optimization to distribution Optimal Operation Model, is calculated using artificial fish-swarm
Method.
In an embodiment of the present invention, the Metropolis criterion in simulated annealing artificial fish-swarm is introduced to calculate
In the foraging behavior of method, artificial fish-swarm algorithm is improved.
Outside the advantages of maintaining traditional AFSA, while solving Artificial Fish and purposelessly random walk and being easily trapped into
The problem of local optimum, makes the operational efficiency of algorithm and solution quality be increased dramatically.
In an embodiment of the present invention, modelling factors are introduced into artificial fish-swarm algorithm, by the office of ASFA and SA at low temperature
Portion's search organically blends.
Have the advantages that quality height, initial value strong robustness, local search ability are strong using SA, obtains the precision of Solve problems
To promotion.I.e. in each generation, Artificial Fish finds state Xi corresponding to globally optimal solution by various actions, then should
State carries out simulated annealing operation, realizes local optimal searching.
In an embodiment of the present invention, according to the prediction of photovoltaic power generation power output, wind power output prediction and load prediction according to general
Rate density function and its Cumulative Distribution Function prediction, introduce distribution Optimal Operation Model for uncertain factor.
Consider Demand Side Response uncertainty when, have EV user participate in scheduling, EV network, off-network and network when power
The equal Normal Distribution of the SOC of battery [17] then participates in the EV quantity of scheduling at each moment and schedulable capacity all has difference
It is different.Similarly, it is assumed that flexible load participates in the number of users and the equal Normal Distribution of reduction of dispatching of power netwoks, in the load peak period
Each moment, which participates in the number of users of scheduling and scheduling capacity, can change.
In an embodiment of the present invention, when establishing distribution Optimal Operation Model, introducing can reduction plans and transferable negative
Two class flexible load of lotus, the calculating that flexible load dispatches cost use step compensation mechanism, and excitation flexible load user participates in needing
Side is asked to respond.
The compensation mechanism can repeat the reimbursement for expenses for being superimposed with a ladder when calculating reimbursement for expenses, as flexible load is used
The increase of family load reduction, income obtained be it is incremental, further motivate user participate in dispatching of power netwoks it is positive
Property.
It should be understood by those ordinary skilled in the art that: the discussion of any of the above embodiment is exemplary only, not
It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples；Under thinking of the invention, above embodiments
Or can also be combined between the technical characteristic in different embodiments, step can be realized with random order, and be existed such as
Many other variations of the upper different aspect of the invention, for simplicity, they are not provided in details.
In addition, to simplify explanation and discussing, and in order not to obscure the invention, it can in provided attached drawing
It is connect with showing or can not show with the well known power ground of integrated circuit (IC) chip and other components.Furthermore, it is possible to
Device is shown in block diagram form, to avoid obscuring the invention, and this has also contemplated following facts, i.e., about this
The details of the embodiment of a little block diagram arrangements be height depend on will implementing platform of the invention (that is, these details should
It is completely within the scope of the understanding of those skilled in the art).Elaborating that detail (for example, circuit) is of the invention to describe
In the case where exemplary embodiment, it will be apparent to those skilled in the art that can be in these no details
In the case where or implement the present invention in the case that these details change.Therefore, these descriptions should be considered as explanation
Property rather than it is restrictive.
Although having been incorporated with specific embodiments of the present invention, invention has been described, according to retouching for front
It states, many replacements of these embodiments, modifications and variations will be apparent for those of ordinary skills.Example
Such as, discussed embodiment can be used in other memory architectures (for example, dynamic ram (DRAM)).
The embodiment of the present invention be intended to cover fall into all such replacements within the broad range of appended claims,
Modifications and variations.Therefore, all within the spirits and principles of the present invention, any omission, modification, equivalent replacement, the improvement made
Deng should all be included in the protection scope of the present invention.
Claims (6)
1. a kind of distribution Optimization Scheduling neural network based, which is characterized in that the described method includes:
Determine the optimization aim of distribution Optimized Operation；
Distribution Optimized Operation continuous variable, integer variable are determined according to optimization aim；
According to optimization aim, continuous variable and integer variable, distribution Optimal Operation Model is established；
Determine the constraint condition of distribution Optimal Operation Model；
The running continuous variable data of distribution, integer variable data are acquired, distribution Optimal Operation Model optimize and is asked
Solution obtains optimal distribution Optimized Operation scheme；
Electrically optimized tune is matched in the running continuous variable data of distribution, integer variable data after obtaining Optimized Operation again, training
Spend data set；
By the distribution Optimized Operation data set after training, training distribution Optimal Operation Model, the distribution for obtaining deep learning is excellent
Change scheduling model；
Optimization is carried out to distribution Optimal Operation Model and obtains optimal distribution Optimized Operation scheme.
2. neural network distribution Optimization Scheduling according to claim 1, it is characterised in that: to distribution Optimized Operation mould
When type carries out optimization, using artificial fish-swarm algorithm.
3. neural network distribution Optimization Scheduling according to claim 2, it is characterised in that: will be in simulated annealing
Metropolis criterion be introduced into the foraging behavior of artificial fish-swarm algorithm, artificial fish-swarm algorithm is improved.
4. neural network distribution Optimization Scheduling according to claim 3, it is characterised in that: modelling factors are introduced people
Work fish-swarm algorithm organically blends the local search of ASFA and SA at low temperature.
5. neural network distribution Optimization Scheduling according to claim 1, it is characterised in that: contributed according to photovoltaic power generation
Prediction, wind power output prediction and load prediction predict according to probability density function and its Cumulative Distribution Function, by it is uncertain because
Element introduces distribution Optimal Operation Model.
6. neural network distribution Optimization Scheduling according to claim 1, it is characterised in that: match electrically optimized tune establishing
Spend model when, introducing can two class flexible load of reduction plans and transferable load, flexible load dispatch cost calculating use rank
Terraced compensation mechanism, excitation flexible load user participate in Demand Side Response.
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