CN201369575Y - Wind power dispatching decision support device - Google Patents
Wind power dispatching decision support device Download PDFInfo
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- CN201369575Y CN201369575Y CNU2008202285015U CN200820228501U CN201369575Y CN 201369575 Y CN201369575 Y CN 201369575Y CN U2008202285015 U CNU2008202285015 U CN U2008202285015U CN 200820228501 U CN200820228501 U CN 200820228501U CN 201369575 Y CN201369575 Y CN 201369575Y
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Abstract
The utility model discloses a wind power dispatching decision support device, which comprises an EMS data server, a wind power monitoring data server, a meteorological data server and a dispatching decision server which are interconnected through a network, wherein the EMS data server acquires and stores the operating state information and on-line power data of each hydroelectric generating set and each thermal generating set in grid and the on-line power data of wind farms at real time, and the wind power monitoring data server acquires and stores the operating state information and wind parameters of wind generating sets in each wind farm in the grid at real time; the meteorological data server acquires and stores the predicated wind parameter values released by the meteorologic bureau of each wind farm in the grid at real time, and the dispatching decision server receives the transmitting data of the EMS data server, the wind power monitoring data server and the meteorological data server, predicates the fluctuation capacity of each wind farm, and calculates the peak shaving scheme of the grid so as the ensure the operating state and the on-line power data of the wind generating sets in each wind farm.
Description
Technical field
The utility model relates to the dispatching of power netwoks field, particularly a kind of wind-powered electricity generation scheduling decision supportive device.
Background technology
Wind power technology development country early widespread usage the wind-powered electricity generation forecasting technique, the wind-powered electricity generation forecasting technique is an important component part of carrying out the wind-powered electricity generation management between wind energy turbine set and the electrical network as the important means of electricity volume transaction, power plant's scheduling and power grid control.But the wind-powered electricity generation Predicting Technique mainly in each wind energy turbine set independent utility, does not have any association each other; Wind-powered electricity generation prediction simultaneously is according to the historical data modeling, with the forecasting mode of numerical weather forecast as the intervention amount.But above-mentioned Forecasting Methodology is disperseed output of wind electric field to independent wind energy turbine set or some predicated error differs widely with the wind-powered electricity generation group's that is comparatively concentrated predicated error, can't be used for big capacity, the high wind-powered electricity generation base power prediction of concentrating, especially can not use as regional wind-powered electricity generation scheduling decision.
Zone wind-powered electricity generation scheduling is in order to carry out peak load regulation network in real time.Because the fluctuation of wind power, general way is the load that wind-powered electricity generation is exerted oneself and is considered as bearing at present.After wind farm grid-connected, the reserve capacity in the electrical network need increase the capacity that is used for wind-powered peak regulation.When the ratio of wind-powered electricity generation in the regional power grid is not high, the susceptance of generally not keeping watch in dispatching of power netwoks work is gone into dispatching of power netwoks, the fluctuation of wind power is at random fully for electrical network, and serious situation is exactly the wind power fluctuation at short notice that equals whole wind Denso machine amount of capacity.If it is limited that whole regional power grid can be used for the peak of wind-powered electricity generation, electrical network can't the complete equipilibrium wind energy turbine set power fluctuation the time, just need the power of restriction wind-powered electricity generation injection zone electrical network.And along with the construction in ten million kilowatt of wind-powered electricity generation base, the Northwest, the wind-powered electricity generation capacity accounts for the progressively increase of system installed capacity ratio, the peak load regulation network ability is subjected to very big challenge, must have more effective method carry out the peak load regulation network capability analysis and organize the standby and peak load regulation network of unit.
At present, application has electricity net safety stable evaluating system and network optimization operational decisions system in the electric power system.Wherein there are off-line and real-time two kinds of forms in the safety and stability evaluation system, safety and stability level according to constraints comprehensive assessment operation of power networks, in-problem running status is provided the adjustment scheme, scheme is exerted oneself based on cutter, cutting load and adjusting high-power station, and form can be that electrical network direct action or the load that provides power plant are adjusted scheme.Also there are off-line and real-time two kinds of forms in network optimization operational decisions system, according to network load and constraints according to reducing network loss or purchasing targets such as electric cost and carry out the optimum organization of unit output in the electrical network.But after the wind farm grid-connected operation of big capacity, also there are not inclusion region wind-powered electricity generation scheduling decision supportive device and decision-making technique.
Summary of the invention
The utility model purpose is to provide a kind of wind-powered electricity generation scheduling decision supportive device, and it can realize the stable assessment of real-time power network and decision-making and optimization peak simultaneously.
In order to achieve the above object, the utility model is achieved by the following technical solutions: a kind of wind-powered electricity generation scheduling decision supportive device, it is characterized in that, comprising: by EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, the scheduling decision server of the network interconnection; Each Hydropower Unit in the electrical network, the running state information of fired power generating unit and the online power data of online power data and wind energy turbine set are gathered and stored to described EMS data server in real time; Described wind-powered electricity generation Monitoring Data server, the running state information and the wind parameter of gathering and storing the wind-powered electricity generation unit of each wind energy turbine set in the electrical network in real time; The wind parameter prediction numerical value of each wind energy turbine set in the electrical network of weather bureau issue is gathered and stored to described meteorological data server in real time; Described scheduling decision server receives the transmission data of EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, predict the fluctuation capacity of each wind energy turbine set, calculate the peak of electrical network, determine the running status and the online power of the wind-powered electricity generation unit of each wind energy turbine set with this.
Further improvement in the technical proposal is:
Described wind-powered electricity generation Monitoring Data server is connected by wireless network with the scheduling decision server.
Described meteorological data server is connected by the intel network with the scheduling decision server.
Described EMS data server is connected by the inner proprietary network of electrical network with the scheduling decision server.
The front end of described EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, scheduling decision server is provided with network firewall.
The utility model is being monitored on the big regional wind energy turbine set wind-resources basis in real time, to big regional wind energy turbine set online power prediction and peak load regulation network capability analysis, realize that large-scale wind power is incorporated into the power networks and dispatch DSS under the condition, under wind power fluctuates widely situation, electrical network guarantees power network safety operation to the control method and the peak load regulation network scheme optimization result of wind-powered electricity generation unit output.
Description of drawings
Fig. 1 is a wind-powered electricity generation scheduling decision back-up system structure chart.
Fig. 2 is a wind-powered electricity generation scheduling decision back-up system workflow diagram.
Embodiment
With reference to Fig. 1, wind-powered electricity generation scheduling decision supportive device comprises: by EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, the scheduling decision server of the network interconnection; Each Hydropower Unit in the electrical network, the running state information of fired power generating unit and the online power data of online power data and wind energy turbine set are gathered and stored to described EMS data server in real time; Described wind-powered electricity generation Monitoring Data server, the running state information and the wind parameter of gathering and storing the wind-powered electricity generation unit of each wind energy turbine set in the electrical network in real time; The wind parameter prediction numerical value of each wind energy turbine set in the electrical network of weather bureau issue is gathered and stored to described meteorological data server in real time; Described scheduling decision server receives the transmission data of EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, predict the fluctuation capacity of each wind energy turbine set, calculate the peak of electrical network, determine the running status and the online power of the wind-powered electricity generation unit of each wind energy turbine set with this.
Wind-powered electricity generation Monitoring Data server operated by rotary motion is in wind energy turbine set, and is remote and environment is abominable, is connected more conveniently by wireless network with the scheduling decision server, invests also low; The meteorological data server is directly gathered the data of national meteorological department, is connected by the intel network with the scheduling decision server; The EMS data server is directly gathered the data of electrical network EMS electric energy management system, and electrical network EMS electric energy management system adopts the inner proprietary network of electrical network, and for security consideration, the EMS data server also is connected by the inner proprietary network of electrical network with the scheduling decision server.The front end of EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, scheduling decision server is provided with network firewall, to guarantee the safety of each server.
Scheduling decision server of the present utility model can be realized wind-powered electricity generation online power prediction, three functions of DSS of wind-electricity integration operation peak load regulation network capability analysis and wind-electricity integration operation power grid security, and be the relation of recursion successively basically between each function, the real-time Monitoring Data of wind energy turbine set online power in wind-powered electricity generation Monitoring Data server wind energy monitoring system data that the survey wind network that utilizes wind-powered electricity generation Monitoring Data server that regional wind energy turbine set wind-resources is monitored is in real time obtained and the electrical network EMS database, utilize situation to carry out real-time assessment to wind energy resources in the zone, and assessment result is combined with wind-powered electricity generation online power prediction system.
Wind-powered electricity generation online power prediction is to carry out forecast model and method research according to the historical wind-resources data of wind energy turbine set, selects suitable forecast model and method, develops the electrical network wind power forecasting system.And carry out wind energy turbine set online power prediction in conjunction with the numerical weather forecast result, with the monitoring system data and the real-time assessment result that survey the acquisition of wind network predicted value is revised then, finally obtain short term power predictions in 24 hours of wind-powered electricity generation base wind energy turbine set online power, the prediction of day energy output, and carry out error analysis and estimation predicting the outcome.By real-time monitoring system the performance of prognoses system is followed the tracks of and monitored simultaneously, the factor of analysis and research impact prediction performance realizes renewal and improvement to system.
Wind-electricity integration operation peak load regulation network capability analysis is according to main hydroelectric plant and thermal power plant's fail safe, economy, mobility and network load characteristic in the electrical network, and according to its workload demand prediction, power supply formation and distribution, the cooperation of water power, thermoelectricity and heat supply unit and wind-powered electricity generation online power prediction result, between main the province, inside the province under the stability limit restrictive condition of section, estimate the peak load regulation network ability, propose technical solution to guarantee power network safety operation, instruct large-scale wind power to insert back dispatching of power netwoks operational mode and change.
The DSS of wind-electricity integration security of operation is used for large-scale wind power and is incorporated into the power networks, based on wind-powered electricity generation online power prediction and peak load regulation network capability analysis result, (assessment of N-1 static security refers under non-failure conditions in the assessment of the N-1 static security of the various constraints of satisfying system safety stable operation and electrical network, disconnect an equipment in the electrical network one by one, to check other equipment whether to transship, if there is not the overload phenomenon, think that then operation of power networks satisfies N-1, be safe aspect this check) prerequisite under, by the wind-powered electricity generation of accepting under the computational analysis electrical network actual motion condition the to predict changing capability of exerting oneself, whether decision-making allows the wind energy turbine set excursion that reaches that prediction is exerted oneself and the acceptable wind-powered electricity generation of electrical network is exerted oneself; The wind-powered electricity generation that allows according to next electrical network is exerted oneself period then, according to the principle that guarantees that the electrical network reserve capacity enough and not makes fired power generating unit compel to stop, optimizes the electric network source adjustment scheme of exerting oneself.
The scheduling decision server is the core of wind-powered electricity generation scheduling decision supportive device, it can obtain data necessary from EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, wind energy turbine set online power is carried out ultrashort phase prediction, calculate the peak modulation capacity under the current system operation mode, determine the peak load regulation network scheme and the controlling level of wind energy turbine set online power.Major function is finished storage and computational analysis exactly, specifically comprises:
1. preserve coal consumption curve, hydroelectric plant's regulating power that Decision Control is calculated required network system model, various system element model and parameter, thermal power plant's operation;
2. receive and handle a plurality of Data Sources, comprising: the wind-resources data of meteorological data server, hydroelectric plant come the current operation of power networks data in water and water level monitoring data and the EMS data server, write local database after the data processing;
3. carry out the ultrashort phase prediction of wind-powered electricity generation online power according to the wind energy turbine set situation of the wind comes from, and provide error maximum under this predicted value;
4. carry out the peak load regulation network capability evaluation according to wind energy turbine set service data and information and wind farm grid-connected power prediction result;
5. carry out topological analysis and state estimation according to operation of power networks data and information;
6. utilize the result of state estimation and coal consumption curve, hydroelectric plant's regulating power of thermal power plant's operation to carry out network optimization calculating, reduce via net loss, and the inspection machine operation level, if existing problems then further calculate corresponding control strategies;
7. the N-1 static security is carried out in operation of power networks and check, if existing problems then further calculate corresponding control strategies;
8. each accident that forecast accident is concentrated is carried out the transient stability judgement, stablizes control strategy accordingly if unstability then further calculates.
9. the scheduling decision server mainly contains 3 software modules compositions: wind-powered electricity generation online power prediction module, peak load regulation network capability evaluation module and wind-electricity integration decision-making module.Finish wind power prediction and peak load regulation network analysis of strategies by these 3 software modules.
It is as follows that the scheduling decision server mainly contains the concrete flow chart of data processing of 3 software modules: finish the ultrashort phase prediction of wind-powered electricity generation online power by the power prediction module, finish the peak load regulation network capability evaluation by the peak modulation capacity evaluation module, and provide peak, finish security evaluation after electrical network is implemented peak by the wind-electricity integration decision-making module.Finally provide the peak of the loss minimum that satisfies the power network safety operation requirement.
(1) used basic data source in this device
Wind-powered electricity generation Monitoring Data server provides wind direction from each monitoring point of wind energy turbine set to this device, wind speed, temperature, air pressure, humidity etc. in real time, historical data, and corresponding unit operation, maintenance information;
EMS data server each Hydropower Unit in this device provides electrical network, the running state information and the online power data of fired power generating unit, and in the electrical network each transformer station, power plant voltage, electric current, burden with power, load or burden without work etc. all in real time and historical data and corresponding equipment operation, maintenance information, provide the online power of wind energy turbine set to reach historical data in real time simultaneously;
The meteorological data server provides the air variation tendency of meteorological department according to fluid mechanics and calculation of thermodynamics obtain to this device, comprises predicted values such as wind direction, wind speed, temperature, humidity, air pressure.
(2) wind power prediction
The prediction of high accuracy, big regional extent wind power be with numerical weather forecast result and on-line monitoring to wind speed, wind power, wind-powered electricity generation can be sent to prediction module with data such as unit quantity, in conjunction with historical data, the utilization forecast model needs data to predict realization to wind power etc. by prediction module.It can provide the prediction of short-term and ultrashort phase.
At first the weather condition predicted of logarithm value weather forecast and synchronization on-line monitoring to data such as wind direction, wind speed, temperature, air pressure compare, determine the wind comes from relation between wind direction, wind speed, temperature, air pressure and the numerical weather forecast of wind energy turbine set, determine the wind energy turbine set forecast model of the wind comes from this.
Choose the historical wind-resources data of wind energy turbine set then, historical summaries such as the power data of corresponding period wind energy turbine set and available unit capacity, model, for incomplete wind-resources data, can also calculate by historical meteorological data and ground table status with the mesoscale model computational methods to obtain replenishing.Carry out forecast model and method research after gathering above-mentioned data, determine the relation between the wind-powered electricity generation base online power and the wind direction of the wind comes from, wind speed, temperature, the air pressure, determine the wind power forecast model with this.
In the actual motion,, carry out the prediction of the wind comes from of next period, and change real-time wind energy monitoring resource data according to wind speed and revise, obtain each wind energy turbine set and predict the situation of the wind comes from assessment result according to the forecast model of the wind comes from based on the result of numerical weather forecast.
Can be with fan capacity, model and the prediction situation of the wind comes from based on each wind energy turbine set, carry out next period zone wind-powered electricity generation online power prediction according to the wind power forecast model, and exert oneself and real-time wind energy monitoring resource data and assessment result are revised according to wind-powered electricity generation is actual, and carry out error analysis and estimation to predicting the outcome, and, stop the electrical network reserve capacity according to the prediction wind-powered electricity generation minimum result that exerts oneself with exert oneself short-term forecast result output of wind-powered electricity generation.
Wind-powered electricity generation ultrashort phase of exerting oneself is predicted the outcome and offers peak load regulation network capability analysis module.
When peak load regulation network capability analysis module prompting peak load regulation network has problem, when needing restriction wind-powered electricity generation online power, predict the outcome and provide it to peak load regulation network capability analysis module with limits value correction wind-powered electricity generation ultrashort phase of exerting oneself.
(3) wind-electricity integration operation peak load regulation network capability evaluation
Water, the maximum of fired power generating unit, minimum technology are exerted oneself peaking performance data such as governing speed in the collection electrical network.Predicting the outcome with network load prediction, start mode and wind power is the basis, real-time analysis, estimates the peak load regulation network ability, proposes the problem and the otherwise effective technique solution that exist.
By real time execution situations such as scheduling energy collecting system monitoring network load, unit outputs.When wind-powered electricity generation is exerted oneself variation, according to the exert oneself prediction and consider the worst error influence of predicted value of next period of wind-powered electricity generation, exert oneself according to the stock of the hydroelectric plant water yield, maximum, the minimum of coming the water yield and thermal power plant to reach, whether the existing reserve capacity of evaluating system can satisfy the variation that wind-powered electricity generation is exerted oneself, if the variation that electrical network can the balance wind-powered electricity generation be exerted oneself then enters the wind-electricity integration decision-making module.
If can not give balance according to unit performance, then computational analysis, the problem that proposition may exist, need the control wind-powered electricity generation to exert oneself, determine controlling level, this controlling value and the wind-powered electricity generation predicted value of exerting oneself is compared, if controlling value is little, illustrate that wind-powered electricity generation exerts oneself excessively, need the restriction wind-powered electricity generation to exert oneself, and the wind-powered electricity generation controlling value of exerting oneself is fed back to prediction module in order to revise the ultrashort phase predicted value of wind-powered electricity generation online power; If controlling value is big, illustrate that wind-powered electricity generation exerts oneself too for a short time, still and do not have no idea to increase wind-powered electricity generation again under the situation of wind and exert oneself, can only take other stringent effort, and after taking measures, carry out the peak load regulation network capability evaluation once more, but till electrical network balance wind-powered electricity generation is exerted oneself.Go forward side by side into the wind-powered electricity generation decision-making module that is incorporated into the power networks.
There is the safety and stability problem in operation of power networks after peak is carried out in the prompting of wind-electricity integration decision-making module, need be that constraints is carried out the peak modulation capacity assessment again with electricity net safety stable assessment existing problems.
(4) wind-electricity integration decision-making module
According to wind power prediction and wind-electricity integration operation peak load regulation network capability analysis result, according to reducing the target that system's coal consumption, via net loss and hydroelectric plant do not abandon water, the extreme misery electricity is exerted oneself in the optimization electrical network, determines the peak load regulation network scheme with this.
According to the peak load regulation network scheme electrical network is carried out safety and stability evaluation, if electrical network satisfy safe and stable operation various constraints and the N-1 static security check, then export the value of exerting oneself and peak load regulation network scheme that wind-powered electricity generation online power allows; If safety and stability evaluation existing problems then propose concrete problem, and possible solution, and, return again given peak regulation evaluation module with this constraints not.
With reference to Fig. 2, illustrate that the concrete workflow of wind-powered electricity generation scheduling decision of the present utility model is:
In the 1st step, select whether manually input: promptly determine real-time research mode, or the off-line research mode: if select then not enter real-time research mode, change the 2nd step wind-powered electricity generation online power prediction; If select to be, then enter the off-line research mode, changeed for the 17th step.
The 2nd step, wind-powered electricity generation online power prediction: carry out wind energy turbine set online power prediction according to operation of power networks real time data and wind energy turbine set the wind comes from, exert oneself situation and numerical weather forecast, provide short-term forecast result and ultrashort phase to predict the outcome.Wherein, entered for the 3rd step with short-term forecast result output; With the output that predicts the outcome of ultrashort phase, entered for the 5th step.
The 3rd step, output wind-powered electricity generation online power short-term forecast result: entered for the 4th step.
In the 4th step, determine electrical network reserve capacity and start mode: with wind-powered electricity generation predict the outcome and electrical network in the hydroelectric plant come the regimen condition, the unit maintenance situation is arranged water in the electrical network, fired power generating unit start mode and reserve capacity.Entered for the 7th step.
In the 5th step, the output wind-powered electricity generation online ultrashort phase of power predicts the outcome: entered for the 6th step and the result was exported to for the 14th step simultaneously.
In the 6th step, revise the ultrashort phase to predict the outcome: predict the outcome with the ultrashort phase of wind power controlling value correction, entered for the 7th step; When entering this step for the first time, no correction value, the wind-powered electricity generation online ultrashort phase of power of directly exporting in the 5th step predicts the outcome, and enters for the 7th step.
The 7th goes on foot the peak load regulation network capability analysis: predicted the outcome by power system operating mode, reserve capacity and wind-powered electricity generation online ultrashort phase of power the peak load regulation network ability is assessed, and entered for the 8th step.
In the 8th step, judge the peak load regulation network ability: when the electrical network reserve capacity fluctuates needed pondage greater than wind-powered electricity generation, judge that electrical network possesses peak modulation capacity, entered for the 9th step; When the electrical network reserve capacity fluctuates needed pondage less than wind-powered electricity generation, judge that electrical network does not possess peak modulation capacity, entered for the 13rd step.
In the 9th step, optimize the peak load regulation network scheme: according to the pondage of wind-powered electricity generation fluctuation needs, according to reducing the target that system's coal consumption, via net loss and hydroelectric plant do not abandon water, the extreme misery electricity is exerted oneself in the optimization electrical network, determines the peak load regulation network scheme with this, enters for the 10th step
The 10th step: judge the power grid security level: electrical network is carried out safety and stability evaluation according to the peak load regulation network scheme, if electrical network satisfy safe and stable operation various constraints and N-1 static security assessment, then export the value of exerting oneself and peak load regulation network scheme that wind-powered electricity generation online power allows, and entered for the 19th step; If the safety and stability evaluation existing problems, promptly electrical network is dangerous, then enters for the 11st step
In the 11st step, determine the electricity net safety stable problem: judge that when the 10th step there is safety problem in electrical network, then propose concrete problem, and possible solution, entered for the 12nd step.
In the 12nd step, increase power grid security constraints: propose influence the problem and the measure of electric power netting safe running, and be constraints, return the 8th and go on foot with the measure.
The 13rd step, control wind-powered electricity generation unit output: owing to predict the outcome according to the ultrashort phase of wind-powered electricity generation, electrical network does not possess peak modulation capacity, with the possible peak modulation capacity of electrical network as constraints, provide electrical network and possess the wind-powered electricity generation online power that allows under the peak modulation capacity condition, entered for the 14th step.
Whether in the 14th step, judge that wind-powered electricity generation is exerted oneself can control: wind-powered electricity generation is exerted oneself and only may be reduced, and can not increase, so relatively control the size of exerting oneself with predicted value and get final product.Predicted value is big, illustrates that then directly the control wind power gets final product, and enters for the 15th step; Predicted value is little, and then explanation need be taked load shedding or call emergency control measure such as other power supplys, enters for the 16th step.
In the 15th step, control wind-powered electricity generation online power: the result is this step result output with the output of the 13rd step, returns for the 6th step.
In the 16th step, given electrical network emergency control: the wind-powered electricity generation online ultrashort phase of power according to satisfied the 6th step output predicts the outcome, and to call emergency use earlier, the principle of load is limited in the back, provides the emergency control measure that electrical network must be taked, and returns for the 7th step as condition.
In the 17th step, manual setting wind-powered electricity generation online performance number: selected the off-line research mode of manual setting in the 1st step, the artificial given wind-powered electricity generation online power of wanting under the research mode in this step directly entered for the 6th step and and the result is exported to the 14th goes on foot.
The 18th step, manual setting power system operating mode: the off-line research mode of in the 1st step, having selected manual setting, the artificial given power system operating mode that will study in this step, can make amendment under the real-time power network operational mode obtains, and the result was exported to for the 7th step.
The 19th step, output final result: after estimating by power grid security in the 10th step, the wind-powered electricity generation online power and the peak that allow are exported as final result, also will be exported the emergency control measure to what taked the electrical network stringent effort.
Claims (2)
1, a kind of wind-powered electricity generation scheduling decision supportive device is characterized in that, comprising: by EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, the scheduling decision server of the network interconnection; Described wind-powered electricity generation Monitoring Data server is connected by wireless network with the scheduling decision server; Described meteorological data server is connected by the intel network with the scheduling decision server; Described EMS data server is connected by the inner proprietary network of electrical network with the scheduling decision server.
2, a kind of wind-powered electricity generation scheduling decision supportive device according to claim 1 is characterized in that, the front end of described EMS data server, wind-powered electricity generation Monitoring Data server, meteorological data server, scheduling decision server is provided with network firewall.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102570453A (en) * | 2012-01-06 | 2012-07-11 | 甘肃省电力公司风电技术中心 | Short-term wind power prediction method and system based on multiple numerical weather prediction sources |
| CN103107560A (en) * | 2011-11-15 | 2013-05-15 | 华北电力科学研究院有限责任公司 | Wind power access method and system based on heat supply unit participating power grid peak shaving |
| CN103577893A (en) * | 2013-11-05 | 2014-02-12 | 国家电网公司 | Energy-saving optimization method for new energy and thermal power bidirectionally supplying power for high energy carrying loads |
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| CN105354761A (en) * | 2015-11-02 | 2016-02-24 | 山东大学 | Safety and effectiveness evaluation method and system for accessing wind-power into power grid |
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| CN107785929A (en) * | 2017-10-13 | 2018-03-09 | 中国华能集团清洁能源技术研究院有限公司 | A kind of solar energy thermal-power-generating station power prediction system and operation method |
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| CN103107560B (en) * | 2011-11-15 | 2015-08-12 | 华北电力科学研究院有限责任公司 | A kind of wind power integration method and system participating in peak load regulation network based on thermal power plant unit |
| CN103107560A (en) * | 2011-11-15 | 2013-05-15 | 华北电力科学研究院有限责任公司 | Wind power access method and system based on heat supply unit participating power grid peak shaving |
| CN102570453A (en) * | 2012-01-06 | 2012-07-11 | 甘肃省电力公司风电技术中心 | Short-term wind power prediction method and system based on multiple numerical weather prediction sources |
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| CN105354761A (en) * | 2015-11-02 | 2016-02-24 | 山东大学 | Safety and effectiveness evaluation method and system for accessing wind-power into power grid |
| CN105354761B (en) * | 2015-11-02 | 2020-03-20 | 山东大学 | Safety and efficiency evaluation method and system for accessing wind power into power grid |
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