CN107748943A - A kind of grid power load management Forecasting Methodology based on cloud computing - Google Patents
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Abstract
The invention provides a kind of grid power load management Forecasting Methodology based on cloud computing, cloud platform is disposed first, load data is managed and handled using cloud computing software, prediction data is shared to the user of needs, compared to traditional grid power load management method, cloud computing computation capability in this method can be with the acquisition network load data of higher efficiency, and due to the characteristic of cloud computing, maintenance and upgrade need not be carried out to hardware devices such as the servers in conventional management Forecasting Methodology, operating cost can further be reduced, with theory value and realistic meaning.
Description
Technical field
The invention belongs to grid power load management field, and it is pre- to refer in particular to the grid power load management based on cloud computing
Survey method.
Background technology
Cloud computing be to network, configurable shared computing resource pond can easily, with the one kind that need to be accessed
Pattern.Multiple mechanisms of the world are all studied cloud computing technology, and are applied to many-sided field, and cloud computing is that one kind is based on
The large-scale distributed computation schema of internet, compared with traditional calculations pattern, it can integrate large scale scale heterogeneous calculating money
Source includes virtualization with servicing and being easy to dynamic expansion.Therefore cloud computing has good economic benefit in power system,
Have broad application prospects.
Network load management prognostic is a classical research topic, and load prediction is one in Economic Dispatch
Item important content, is EMS（EMS）An important module.The groundwork of load prediction is according to electric power
The past and present numerical value of load goes to speculate future values, therefore the object studied is a uncertainty event.For it is such not
Event is determined, people need to use suitable Predicting Technique, infer the development trend of load and situation about being likely to be breached.Accurately
Load prediction can economically arrange the start-stop of power network internal generator group, keep the security and stability of operation of power networks, close
Reason arranges unit maintenance, reduces unnecessary work, improves economic results in society.
The content of the invention
With the development of intelligent grid, load prediction has welcome new challenge.Compared with traditional power network is predicted, in traditional electricity
On the basis of net Techniques for Prediction of Electric Loads, the prediction object more diversification that is related in intelligent grid, including new energy and point
The cloth energy it is grid-connected, so intelligent grid load prediction technology should be more intelligent, precision of prediction requires higher, therefore newly
Type grid power load forecasting method is particularly important.
In order to solve the above problems, the present invention proposes a kind of grid power load management prediction side based on cloud computing
Method, can effectively it overcome the shortcomings of in traditional load forecasting method, methods described concretely comprises the following steps.
Step 1, load forecast cloud is disposed.
Step 2, the data of collection are collected and stored.
Step 3, network load is analyzed and predicted by cloud computing.
In the step 1, " cloud " includes service and management.
Network load prediction cloud based on cloud computing is a kind of privately owned cloud service for analyzing, managing, predicting.It is described
Service includes IaaS, PaaS and SaaS three parts, and wherein IaaS (infrastructure is to service) comes with the virtual technology of cloud computing
Resource management.Wherein PaaS (platform services) uses Hadoop framework, and the hardware device cluster virtually built is built into intelligence
The running environment of energy network load prediction.Wherein SaaS (software services) sets intelligent grid load service, such as load point
Analysis, load prediction service etc..
The management mainly includes being managed charging, service, user and safety etc..Wherein accounting management is to each
Class user carries out expense statistics to resource and the service condition of service, it is ensured that user can rationally obtain service, and service management refers to
In the case where ensureing normal operation, service quality is lifted, user management refers to account management of user etc..
Data in the step 2 include load data, environmental data, economic data etc..Pass through distributed storage (GFS)
Carry out data storage.
GFS is an expansible distributed file system, for it is large-scale, distributed, mass data is visited
The application asked.It is run on cheap common hardware, and provides fault tolerance, can to a large number of users provide overall performance compared with
High service.
Load prediction includes pre- to bus load prediction, large user's load prediction, generation of electricity by new energy load in the step 3
Survey.
Adaptive forecasting technique needs to initially set up Load Forecast Algorithm storehouse.Established according to the change of load more flexible
Forecast model.The polytropy of load is adapted to the variation of model.
The bus load prediction is by classifying to different qualities bus, the abnormal data to become more meticulous being divided
Analysis and modification, record the different affecting factors that different buses are subject to, establish correlation factor information storehouse, by select forecast model and
Model parameter is determined, decision-making is provided for bus load prediction.
Large user's load prediction, so-called large user high load capacity industry, electricity consumption such as steel plant and high-speed railway are born
Lotus carries randomness, larger to power network impact, and compared with general regional network load, this kind of large user generally has multimodal special
Sign.For users such as steel plant analysis record can be carried out to its production schedule and repair schedule, can be right for high ferro electricity consumption
The schedule and operation plan of high ferro carry out analysis record.And then data are used with the prediction based on learning method and probabilistic method
Model, current moment is gone out to the load extreme value of more peak characters and size is predicted.
So-called generation of electricity by new energy load prediction includes wind power generation, photovoltaic generation load prediction.Wherein wind power generation is defeated
Go out characteristic to show as with randomness, intermittence, it is affected by environment larger, multimodality is typically presented.The fluctuation of wind-power electricity generation
There is direct relation by wind speed, wind power prediction can carry out forecasting wind speed by the data of weather station, pass through multiple wind power plants
The record analysis of data, screening assimilation characteristic, wind-power electricity generation is carried out by the adaptive combination forecasting based on study
Load prediction.Photovoltaic generation load prediction is similar to wind power generation load prediction, by weather station data, obtains weather data simultaneously
Establish position of sun model and then carry out intensity of solar radiation prediction, and then carry out photovoltaic generation load prediction.
Brief description of the drawings
Fig. 1 is the network load management prognostic method overall flow figure of the invention based on cloud computing.
Fig. 2 is the prediction scheme structure chart to bus.
Fig. 3 is the prediction scheme structure chart to large-scale user.
Fig. 4 is to concentrating wind power generation load prediction structure chart.
Fig. 5 is to distributed wind power generation load prediction structure chart.
Fig. 6 is to photovoltaic generation load prediction structure chart.
Specific embodiment
The core concept of the present invention is that structure cloud computing model is managed and predicted to grid power load, so as to true
Reach the optimization collocation of cost and interests on the premise of protecting the stabilization of power grids.
Network load management prognostic method of the invention based on cloud computing as shown in Figure 1 includes disposing electric load first
Secondly prediction cloud is managed and stored to the data of collection is analyzed and is predicted to network load finally by cloud computing.
In step 1, " cloud " includes service and management.The service includes IaaS, PaaS and SaaS three parts.
IaaS is the virtualized infrastructure part based on cloud computing, is substantially carried out resource management.Classical forecast is managed
Hardware facility have server and information storing device etc..IaaS is monitored by the virtualization means such as virtual machine to resource,
Scheduling.
Maintenance cost and office space can be saved by IaaS services, make the focus of work from safeguarding that hardware device is converted to
The maintenance of virtual unit, greatly reduces workload, economizes on resources.
Load forecast cloud build use CloudStack, CloudStack be one increase income there is High Availabitity
The cloud computing platform of property and autgmentability.The platform supports to manage at present the hypervisor of most of main flow, such as KVM virtual
Machine, XenServer, VMware, OracleVM, Xen etc..
Virtualization technology uses virtual machine KVM, and KVM is integrated into the hypervisor of linux kernel, be X86-based and
The Linux of hardware supported virtualization technology fully virtualized solution have nothing to do Linux a very little module, can utilize
Linux such as does task scheduling, memory management, interacted with hardware device at the work.It can operate with the structure of network load prediction cloud.
The server built in units of virtual machine, database server group are predicted as network load with PaaS
Running environment.
The management (Cloud Management) mainly includes being managed charging, service, user and safety etc..
Wherein accounting management is to carry out expense statistics to resource and the service condition of service to all types of user, it is ensured that user can rationally obtain
Service, service management refer in the case where ensureing normal operation, lift service quality, and user management refers to the account management of user
Deng.
Data Collection includes load data, environmental data, economic data etc. in the step 2.
For the distributed data storage technology of cloud computing based on Google GFS, GFS is a kind of expansible distributed document
System, redundant storage is taken to preserve data.One GFS cluster is made up of a master and substantial amounts of chunkserver,
And by many clients（Client）Access.
Master and chunkserver is typically the Linux machines for running client layer service processes.As long as resource and can
Allow by property, chunkserver and client may operate on same machine.File is divided into the block of fixed size.Often
Individual block is identified by a constant, globally unique chunk-handle of 64, and chunk-handle is when block creates
By master distribution.ChunkServer by block as Linux files be stored in local disk and can read and write by
The data that chunk-handle and position section are specified.Consider that each block is copied to multiple for reliability
On chunkserver.Under default situations, 3 copies are preserved, but this can be specified by user.
GFS greatlys save memory space compared to traditional mode.
SaaS services are software layer service, and being realized by cloud computing includes load Analysis, load prediction and load management
Service, specific implementation are as follows.
In the step 3, analysis and prediction to network load include analyzing bus load and predicting, large user's load
Analysis and prediction, generation of electricity by new energy load Analysis and prediction.
Bus load takes scheme as shown in Figure 2, and the bus of different qualities is classified, to the abnormal number to become more meticulous
According to being analyzed and changed.
Research influences the factor of bus load, the meteorological data with weather station or the numerical value using Numerical weather forecasting
（NWP）.
Bus load abnormal data algorithms library is established, records and recognizes abnormal data to find amendment scheme.
The set of power network bus is subjected to classification analysis, determines industry load, voltage class and influence factor.Establish bus point
Class database, load index analysis is carried out to data in database.Influence factor is analyzed with reference to exception database.
With the implementation of national sustainable development policy, the Main Means using high parameter equipment as industrial upgrading.Its
The electric load of middle large user such as steel plant etc. rises therewith.And there is certain randomness and unstability.
With the development of high-speed railway, the impact load brought has a certain impact to power network prediction.Need high ferro band
The impact load come is taken into account.
Scheme shown in Fig. 3 can be used to solve the above problems, the large users such as steel plant and Gao iron loads are needed first
Response analysis is asked, for the factory class large user such as steel plant, its production schedule and maintenance plan is analyzed and recorded, for
High iron load, the schedule and operation plan of high ferro are analyzed.And then data are used learning method and probabilistic method for
Main forecast model, current moment is gone out to the load extreme value of more peak characters and size is predicted.
Wind energy in generation of electricity by new energy is sent out with centralized wind power generation and distributed wind power generation with scattered small-sized fan
Based on electricity, distributed wind power generation has larger randomness and unstability.Wind power prediction makes full use of centralized wind
The weather station data and historical wind speed data of electric field, the analysis result of distributed wind-powered electricity generation and NWP.Concrete scheme is as shown in figure 4, profit
With meteorological department and NWP to the prediction data of weather, NO emissions reduction processing is carried out.By the record analysis of multiple wind farm datas,
Screening assimilation characteristic, scale processing is risen by the adaptive combination forecasting based on study and born and then progress wind-power electricity generation
Lotus is predicted.Distributed power generation load forecast scheme is as shown in figure 5, according to NWP to wind speed, the air pressure of multiple distributed wind-powered electricity generations
Counted with temperature information, carry out Study on Relative Factors, wind-force is carried out by the adaptive combination forecasting based on study
Generation load tentative prediction, then analysis is contributed to result by the history based on region wind-powered electricity generation prediction result and based on time series
It is modified, obtains the prediction result of distributed wind-powered electricity generation load.
Photovoltaic generation load prediction scheme in new energy is as shown in fig. 6, meteorological according to the data statistics periphery of weather station
Historical data data, and intensity of solar radiation data, data assimilation screening is carried out, with reference to position of sun model construction solar radiation
Strength model.
Photovoltaic generation demand history data information is counted, with reference to above-mentioned intensity of solar radiation model to photovoltaic generation power load
Lotus is predicted.
Claims (5)
1. the invention provides a kind of grid power load management Forecasting Methodology based on cloud computing, it is characterised in that：Using cloud
The virtual technology of calculating replaces the entity hardware in traditional load forecasting method, reduces operation expense, improves the utilization of resources
Rate.
A kind of 2. grid power load prediction management method based on cloud computing according to claim 1, it is characterised in that：
Comprise the following steps that.
3. step 1, load forecast cloud is disposed,
Step 2, the data of collection are collected and stored,
Step 3, network load is analyzed and predicted by cloud computing.
4. according to claim 2 in step 1, the network load prediction cloud based on cloud computing be it is a kind of be used to analyzing, manage,
The privately owned cloud service of prediction, the service include IaaS, PaaS and SaaS three parts.
5. load prediction is included to bus load prediction, large user's load prediction, new energy in step 3 according to claim 2
Source generation load prediction.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110086731A (en) * | 2019-04-25 | 2019-08-02 | 北京计算机技术及应用研究所 | A kind of cloud framework lower network data stabilization acquisition method |
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| CN111967688A (en) * | 2020-09-02 | 2020-11-20 | 沈阳工程学院 | Power load prediction method based on Kalman filter and convolutional neural network |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110086731A (en) * | 2019-04-25 | 2019-08-02 | 北京计算机技术及应用研究所 | A kind of cloud framework lower network data stabilization acquisition method |
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| CN111967688B (en) * | 2020-09-02 | 2024-02-23 | 沈阳工程学院 | Power load prediction method based on Kalman filter and convolutional neural network |
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