1420 results about "Generating capacity" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses an offshore wind power plant micro-siting optimization method considering regular layout. The method comprises the following steps: firstly, obtaining wind data, wind power plant parameters, fan parameters and wake flow parameters; performing multi-angle rotation on the wind power plant based on an equivalent rotation coordinate method, constructing a gridding mixed integer quadratic programming model, and performing rotation rule layout optimization (RRLO-1) to obtain an initial fan layout; on the basis of the initial layout, a hybrid algorithm (GA-ISSA) of a genetic algorithm and a sparrow search algorithm is adopted, the position of a draught fan and the rotation angle of the wind power plant are finely adjusted, and fine rule layout optimization (RRLO-2) is completed; and finally, the wake flow influence is calculated through a Cosine wake flow model and a quadratic sum superposition model, a fan power curve is processed in a piecewise linearization mode, and the final layout is output by taking the maximization of the total generating capacity as the target. According to the method, a fan position configuration scheme meeting the engineering rule layout requirement is provided for early-stage planning of the offshore wind plant, the wake effect is reduced, the power generation efficiency is improved, and development of the offshore wind power industry and low-carbon target implementation are facilitated.

The invention discloses an optical storage layered collaborative optimization control method coupling photovoltaic priority absorption and time-of-use electricity price, and relates to the technical field of new energy power system optimization control. The method comprises the following steps: taking a photovoltaic generating capacity prediction value, a load electricity consumption prediction value and peak and valley electricity price information as input data; carrying out rolling optimization by utilizing a model prediction control framework, and generating a light storage plan table; and calculating a photovoltaic output regulation value and energy storage charging and discharging power based on the generated light storage plan table in combination with the photovoltaic power generation power and the load power consumption power which are acquired in real time, executing corresponding energy storage charging and discharging and photovoltaic output processing by applying a decision tree control mechanism, and optimizing light storage control according to a processing result. According to the method, the photovoltaic preferential consumption strategy is executed, the light abandoning amount is reduced, the photovoltaic consumption rate is improved, the time-of-use electricity price dynamic response mechanism and the energy storage efficiency compensation and light abandoning punishment mechanism are combined, peak-valley arbitrage is maximized, meanwhile, the comprehensive electricity utilization cost is reduced, and the power purchase demand of a power grid is reduced.

The invention discloses a wind power plant energy management system and a dispatching optimization system, and belongs to the field of wind power generation. The system comprises a data acquisition module, a wind speed prediction module, a wake effect analysis module, a power prediction and distribution module, an optimization scheduling module, a dynamic adjustment module, an energy efficiency evaluation module and a communication control module which work cooperatively. Through multi-source data fusion and dynamic collaborative optimization, the comprehensive performance of the wind power plant is remarkably improved, on the operating efficiency level, the system combines the space-time convolutional neural network and the multi-target optimization algorithm, high-precision prediction of minute-level wind speed and optimal distribution of whole-field power are achieved, energy loss caused by the wake effect is effectively reduced, and the wind power generation efficiency is improved. The output strategy is dynamically adjusted according to the health state of the fan, and the fatigue loss of the equipment is delayed while the generating capacity is maximized; on the power grid adaptability level, a self-learning mechanism based on the frequency disturbance qualified rate is introduced, and frequency modulation response parameters are optimized in real time.

Disclosed in the present invention are a load-prediction-based control method and apparatus for an energy storage apparatus, and a terminal device and a storage medium. The method comprises: using a preset load prediction model to perform prediction on the basis of real-time operation data, so as to obtain a predicted load value of a micro-grid within a future time period; then, on the basis of a power generation capacity and the predicted load value, determining whether there is surplus electricity after the current power generation capacity of the micro-grid satisfies a future load demand; if so, controlling an energy storage apparatus to be charged, so as to avoid the waste of electric energy; and if not, controlling the energy storage apparatus to supply power to the micro-grid, so as to ensure that the micro-grid can satisfy the future load demand, thereby effectively ensuring the stable power supply of the micro-grid.

The invention discloses a power grid dispatching method based on emergencies and a graph structure. The method comprises the following steps: establishing a baseline load prediction model by collecting historical electrical load data; recognizing emergencies influencing the power utilization demand in real time; and in combination with real-time power consumption behavior data, constructing a time-varying power consumption disturbance factor, and dynamically correcting load prediction. Geographic distribution and historical power generation data of new energy power stations such as wind power and photovoltaic power stations are acquired, a power generation graph structure model is constructed, the power generation amount is independently predicted by adopting a long-short-term memory network, and a prediction result is corrected in combination with a graph structure. And based on the corrected load and power generation prediction, considering the capacity and the energy state of the energy storage system, optimizing multi-time-sequence-scale energy scheduling, and formulating an energy storage charging and discharging and generator set operation scheme. According to the method, the dynamic influence of emergencies and the spatial relevance of new energy are fused, the load prediction precision and the new energy power generation prediction accuracy are remarkably improved, more reasonable power grid dispatching is achieved, and the safety and stability of a power grid are enhanced.

The invention relates to the technical field of new energy grid connection, and discloses a wind power plant power generation performance evaluation method, device and equipment and a storage medium, and the method comprises the steps: calculating the power generation capability parameters of a target wind power plant according to the operation parameters and configuration parameters of the target wind power plant; calculating an economic effect evaluation parameter according to the availability rate of the target wind power plant, the power grid compatibility comprehensive score, the power generation capacity parameter and the health coefficient; and determining a power generation performance evaluation result of the target wind power plant according to the power generation capacity parameter and the economic effect evaluation parameter. According to the power generation performance evaluation method for the wind power plant provided by the embodiment of the invention, the limitation that evaluation is carried out only from a single index of short-term power generation capacity traditionally is avoided in a mode of combining the power generation capacity parameters and the economic effect evaluation parameters, and comprehensive evaluation of the power generation capacity and the economic benefits is realized.

The invention discloses a wind-solar complementary power generation system and method based on intelligent monitoring, belongs to the field of wind-solar complementary power generation, and is used for solving the problems of fixed parameter weight, lack of environment-equipment coupling analysis and large power generation capacity prediction deviation of a traditional system. Generating a standard matching curve template to match the current working condition, and monitoring a working condition fluctuation index to trigger updating; key equipment parameters are screened based on historical data and association degree, an environmental working condition scene association rule base is constructed, generating capacity fluctuation is judged through double references, and a core influence chain is traced; matching historical samples through Euclidean distance to calculate an initial generating capacity predicted value, constructing three types of correction coefficients of environmental deviation, equipment health and a core influence chain, and obtaining a comprehensive correction factor and a correction predicted value; the monitoring accuracy and the fluctuation attribution capability are improved, the prediction deviation is reduced, and the system operation strategy is ensured to adapt to the actual power generation demand.

The invention discloses a park carbon emission data determination method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring observed carbon emissions and carbon source equipment power generation amounts respectively corresponding to multiple types of carbon sources in a target park; determining initial electrical carbon factors corresponding to the multiple types of carbon sources according to the observed carbon emissions corresponding to the multiple types of carbon sources and the power generation amount of the carbon source equipment; determining truncation errors respectively corresponding to the multiple types of carbon sources according to the observed carbon emission, the power generation amount of the carbon source equipment and the initial electric carbon factor respectively corresponding to the multiple types of carbon sources; obtaining target electrical carbon factors corresponding to the multiple types of carbon sources according to the truncation errors corresponding to the multiple types of carbon sources; and according to the observed carbon emissions, the reference carbon emissions and the target electrical carbon factors corresponding to the multiple types of carbon sources, determining target carbon emissions corresponding to the multiple types of carbon sources. According to the invention, the technical problem that the determined park carbon emission data is inaccurate in the prior art is solved.

The invention discloses a scheduling rule optimization method for a water-wind-light integrated system, and relates to the technical field of energy system scheduling. Comprising the following steps: designing a water-wind-light multi-energy complementary long-term scheduling graph, initializing upper and lower scheduling lines of the scheduling graph and deciding output parameters to calculate the monthly total generating capacity; designing a middle-term scheduling rule, and distributing the monthly total generating capacity to each day to obtain daily generating capacity; designing a short-term scheduling rule, and distributing the daily total power generation amount to each hour to obtain a day-ahead power generation plan; determining the load state of each hydropower station in the cascade hydropower stations, and constraining the hydraulic connection and power connection between different hydropower stations to obtain a real-time scheduling strategy; and performing updating iteration optimization on optimization variables of the multi-objective optimization scheduling model to obtain a Pareto solution set, and determining a water-wind-light integrated scheduling rule according to a compromise solution. According to the invention, the risks of power abandoning and load loss in the operation process can be reduced to the greatest extent.

The invention discloses a wind turbine generator self-adaptive control method, medium and system based on environmental turbulence, and the method comprises the steps: S1, collecting the power of a generator and the current pitch angle in the normal operation process of a wind turbine generator; s2, estimating the real-time wind speed based on the power of the generator and the inherent parameters of the unit; s3, calculating real-time environmental turbulence intensity according to the real-time estimated wind speed; s4, inputting the real-time environmental turbulence intensity and the current pitch angle into a fuzzy controller, and calculating a variable pitch control parameter coefficient according to a preset fuzzy rule; and S5, based on the variable-pitch control parameter coefficient, the variable-pitch control parameter is adjusted to carry out variable-pitch control, and self-adaptive stable operation of the unit is achieved. According to the method, no hardware needs to be additionally installed, unit load impact and rotating speed fluctuation caused by large turbulence can be effectively restrained, and on the premise that it is ensured that the limit load of key components does not exceed the limit, the operation stability of the unit is improved, the power generation loss is reduced, and cost reduction, efficiency improvement and safe and stable operation are achieved.

The invention relates to a wind power plant active wake flow control method and device based on machine-field cooperation. The method is characterized in that a field control layer and a machine control layer are cooperatively operated; calculating the real-time wind speed of each fan in the wind power plant based on a Gaussian rotation-energy conservation wake flow superposition model describing the asymmetric characteristics of the wake flow and the multi-wake flow interactive superposition of the whole wind power plant; in the field control layer control, the optimal yaw angle under different wind condition combinations is obtained by taking the maximization of the total output power of the wind power plant as a target; in the control of the machine control layer, yaw angle tracking errors and the fatigue load of a yaw bearing are considered, model prediction control is applied within a continuous time range, and the yaw angle of each fan is dynamically adjusted according to the optimal yaw angle provided by the field control layer. Compared with the prior art, the method has the advantages that the generating capacity is remarkably improved and the fatigue load is reduced in a randomly fluctuating wind field environment, and the method has remarkable applicability in a large offshore wind plant.

The utility model provides a peak-shaving steam supply system for deep waste heat utilization of a thermoelectric unit, which deeply recovers cold end waste heat and flue gas waste heat of a cogeneration unit, is coupled with a two-stage compression heat pump, extracts waste heat to heat demineralized water, and serves as a primary heat source for supplying industrial steam, so that the waste heat participates in external heat supply; the superheater utilizes the generating capacity of the unit to enable industrial steam to reach steam supply parameters, the waste heat of the unit and the generating capacity are combined for external steam supply, the circulating heat efficiency is improved, the steam supply capacity is improved, meanwhile, the on-grid electricity is reduced, thermoelectric decoupling is achieved, the unit adjusting operation interval is expanded, the operation cost and pollutant emission are reduced, and peak regulation or spot transaction earnings are improved. And a clean, low-carbon, flexible, efficient, economical and energy-saving combined heat and power generation system is constructed.

The invention relates to the technical field of new energy power generation, and discloses a generating capacity calculation method and system based on wind and light resources and equipment data. The method comprises the following steps: acquiring and fusing wind and light resource data and equipment performance parameters to construct a standardized data set; based on the data set, a resource-equipment dynamic coupling potential field is established, and the potential field accurately represents the comprehensive power generation potential in the space-time dimension by fusing the resource space gradient and the equipment response characteristics; carrying out triple integral calculation on the potential field, and controlling an error by adopting a self-adaptive algorithm; and finally, realizing self-calibration of the system through model verification and parameter optimization. According to the method, the defects of insufficient data fusion, model staticization, contradiction between calculation precision and efficiency and the like of a traditional method are overcome, and the accuracy, the adaptive capacity and the engineering practical value of generating capacity calculation are remarkably improved.

The invention provides a dynamic load prediction and optimal scheduling method and system for charging and discharging of an energy storage power station, and belongs to the technical field of industrial big data. According to the method, power grid operation related parameters such as power grid demand signals, market electricity price and generating capacity are obtained and subjected to time sequence feature processing to generate a time sequence feature sequence, whether the fluctuation amplitude of the time sequence feature sequence exceeds a preset fluctuation amplitude threshold value or not is judged, and if yes, uncertain factors are quantized by adopting a Monte Carlo simulation algorithm, and the power grid operation related parameters are obtained. The problem that parameter fluctuation uncertainty is neglected in traditional load prediction is effectively solved, the constructed load prediction model is more suitable for an actual power grid operation scene, and the reliability and accuracy of a load prediction result are greatly improved. Moreover, based on the comparison between the residual capacity percentage of the energy storage equipment and the preset percentage threshold, the charging and discharging operation is dynamically decided, so that the risk of insufficient power supply capability when the residual capacity of the energy storage equipment is too low is avoided, and resource idling when the residual capacity of the energy storage equipment is too high is prevented.

This invention provides a graph neural network-based distributed solar power output prediction method and system that improves prediction accuracy from three aspects: data, models, and parameters, and offers a practical and needs-based solution for highly accurate short-term predictions of distributed solar power plants. [Solution] The method initializes the irradiation data of distributed solar power generation for a set period prior to the current time using spatial kriging interpolation, expands the initialized irradiation data based on a graph-diffuse convolutional network to obtain expanded irradiation data, combines the graph-diffuse convolutional model and a spatial Transformer module using a residual structure to capture spatial features, and uses a trend gate recurrent unit network as input to obtain a prediction result of distributed solar power generation output for a set period after the current time.

This invention relates to the field of power system generation scheduling and discloses a method for risk analysis and control of pre-flood energy storage in cascade hydro-wind-solar complementary systems. Taking the pre-flood energy storage as a constraint, a dry-season drawdown model and a flood-season water storage operation rule are established to define simulation criteria. A comprehensive set of indicators, including dry-season power shortages, flood-season water spillage, inadequate year-end energy storage, and wind and solar power curtailment, is constructed to quantify multi-stage, multi-source operational risks. By coupling Monte Carlo simulation with fuzzy membership functions, the method characterizes multidimensional uncertainty scenarios and their probabilities, and analyzes the quantitative relationship between pre-flood energy storage and system benefits, risk probabilities as well as losses. Simulation results demonstrate that precise risk characterization coupled with proper storage control increases annual generation by 580 million kWh while reducing average risk-induced losses by 42%, demonstrating substantial practicality.

The invention relates to the field of photovoltaic panel cleaning, and discloses a photovoltaic panel intelligent cleaning method and system for photovoltaic operation and maintenance, and the method comprises the steps: carrying out the detection of the state of a photovoltaic panel in a preset time period, detecting the power generation amount, recognizing the dirt distribution, hot spot defects or physical damage, and recording the position of an abnormal region, and obtaining a detection result; a multi-parameter cleaning decision model is established, the detection result is imported into the decision model, and whether a cleaning system is started or not is judged; when it is judged that the cleaning system is started, the photovoltaic panel is cleaned according to the detection result; after cleaning is completed, the state of the photovoltaic panel is subjected to secondary detection through the detection equipment, the generating capacity is detected, and it is confirmed that no stain residues or newly-added damage exists; and after the secondary detection is completed, a detection result and a cleaning result are input into the system, and a data report is generated and uploaded to an upper computer, so that the water, electricity and labor costs are saved, and the precision, automation and intelligentization of the cleaning operation and maintenance of the photovoltaic power station are realized.

The invention discloses a control method for a photovoltaic energy storage system, and the method comprises the steps: selecting an operation mode, the operation mode comprises a first mode, and in the first mode, step 1, judging whether the power generation amount of a photovoltaic system is sufficient or not; 2, if the phase change material is sufficient, the photovoltaic system supplies power to the inverter compressor, the operation frequency of the inverter compressor is controlled, the inverter compressor is shut down until the phase change material in the photovoltaic energy storage system reaches the set temperature, and in the process, the battery is charged with surplus electricity; 3, if not, the operation frequency of the inverter compressor is controlled according to the photovoltaic generating capacity at the current moment and the preset temperature condition, and the inverter compressor is shut down until the phase-change material reaches the set temperature; 4, judging whether the photovoltaic generating capacity at the current moment meets the requirement that the inverter compressor operates at the lowest frequency of the standard working condition or not; and 5, if not, power is supplied to the inverter compressor through the battery and the photovoltaic system at the same time, and the inverter compressor is made to operate at the lowest frequency of the standard working condition and shut down until the phase change material reaches the set temperature or the voltage of the battery is lower than the set voltage value. The technical effect that the endurance of the energy storage system can be greatly prolonged while enough energy storage efficiency is guaranteed is achieved.

Provided are a method and apparatus for detecting a failure of a solar module. The method includes obtaining real-time power generation data of each of a plurality of solar modules included in at least one solar array, setting a reference value using at least one of past power generation data of the plurality of solar modules and specifications of the plurality of solar modules, determining each of the plurality of solar modules to be either a normal solar module or an abnormal solar module by comparing the reference value with a power generation amount of each of the plurality of solar modules, which is calculated using the real-time power generation data, and providing an interface displaying a solar module determined to be the abnormal solar module.

To provide a power supply system that minimizes capital investment.SOLUTION: A power supply system includes a solar power generation device 13 that generates power when irradiated with sunlight, a fuel cell 14 that generates power when supplied with fuel, a secondary battery 15 that can be charged and discharged, and a control unit 16 that controls the solar power generation device 13, the fuel cell 14, and the secondary battery 15. The control unit 16 obtains a power demand forecast of the power that is to be required from the power demand 11 and a solar power generation forecast of the power that is to be generated by the solar power generation device 13, and formulates an operation plan for operating the fuel cell 14, the solar power generation device 13, and the secondary battery 15 in response to the power demand 11 on the basis of the power demand forecast and the solar power generation forecast, and controls the operation of the fuel cell 14 to follow the power demand load of the power demand 11 based on the operation plan.SELECTED DRAWING: Figure 2

The invention relates to the technical field of wind power plant generating capacity prediction. The invention provides a wind power plant generating capacity prediction method and system considering a maintenance plan and an equipment health degree. The method comprises the following steps: collecting multi-source data of a wind turbine generator, and carrying out preprocessing and space-time alignment; based on the health monitoring data, state features are extracted, and a real-time health degree index is calculated by adopting a fuzzy comprehensive evaluation method; optimizing the maintenance plan according to the health degree index, and establishing a mapping relation among the health degree, the maintenance measures and the power generation loss; taking the meteorological data, the historical operation data, the health degree index and the maintenance influence coefficient as input and the historical power generation amount as output, and constructing and training a machine learning prediction model; and inputting data of a to-be-predicted time period to obtain a preliminary prediction result, and dynamically correcting model parameters through deviation analysis to obtain a final power generation prediction value. The problem that the prediction precision is insufficient due to the fact that an existing wind power plant generating capacity prediction method does not systematically consider the dynamic influence of the equipment health state and the maintenance plan is solved.

The invention belongs to the technical field of energy planning, and discloses a coal mining area green electricity direct connection planning method and system, and the method comprises the steps: integrating geographic space information, obtaining a three-dimensional geographic space information database, carrying out the processing through a land recycling evaluation model, obtaining a thermodynamic diagram, and carrying out the quantitative analysis of the thermodynamic diagram, and obtaining the power generation amount. Processing and fusing by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic combined sample; processing by using the load prediction model to obtain a load prediction curve set; a source-load time sequence mismatch quantitative model is input for analysis, and an energy storage collaborative configuration scheme is obtained; inputting a multi-target line optimization model for analysis to obtain a direct connection line topological structure diagram; and performing data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and performing coal mining area green electricity direct connection planning through the source storage load collaborative optimization model. The green electricity is obtained through direct connection, and the cost is reduced.

The invention discloses a hydropower station generating capacity prediction method and system based on knowledge graph guidance, and relates to the technical field of hydropower station generating capacity prediction. Comprising the following steps: S100, in a power generation capacity prediction process, establishing a threshold trigger audit baseline, recording an operation environment state, a knowledge graph edge weight change parameter and a prediction error symbol during prediction deviation triggering each time, generating a trigger fingerprint containing a time identifier, and storing the trigger fingerprint; according to the method, through whole-process auditing, triggering fingerprint tracing, gain analysis and risk label identification, weight dynamic limitation, double-track comparison and closed-loop correction, accurate control and self-optimization of abnormal dependence of the model are realized, the accuracy and the stability of power generation capacity prediction are improved, the correction and protection capability on abnormity is enhanced, and the power generation capacity is improved. And the risk of error accumulation and dependence on curing is effectively avoided.

The invention discloses a novel wave-driven drifting buoy, and belongs to the field of wave energy utilization of an ocean new energy power generation technology. The drifting buoy comprises a spherical floating body assembly, a harvesting energy power generation device, an electric power storage assembly and a data collecting and monitoring system, the harvesting energy power generation device, the electric power storage assembly and the data collecting and monitoring system are installed in the floating body assembly, the floating body assembly is sealed and floats on the water surface, and the harvesting energy power generation device can efficiently convert six-degree-of-freedom motion generated by the floating body assembly under the action of wave energy into electric energy; the electric power storage assembly is used for storing electric energy of the harvesting energy power generation device and supplying the electric energy to external electric equipment, and the data acquisition monitoring system is used for monitoring and positioning the movement of the drifting buoy and feeding monitoring data and positioning information back to the control end. The drifting buoy solves the problems that an existing wave energy power generation device is insufficient in power generation capacity, large in voltage fluctuation range and insufficient in survivability under complex sea conditions due to high direction sensitivity and narrow-band response limitation, and reliability is reduced due to wave impact and salt spray corrosion.

The invention discloses a coal power unit flue gas waste heat power generation carbon dioxide capture comprehensive utilization system, which is characterized in that a flue gas waste heat ammonia steam turbine generator set is replaced by a flue gas waste heat low-temperature nitrogen steam turbine generator set or an expansion machine generator set, low-temperature nitrogen after doing work absorbs heat from flue gas, the flue gas is reduced to a lower temperature, and the flue gas waste heat ammonia steam turbine generator set is replaced by the expansion machine generator set. Carbon dioxide trapped after purification treatment is hydrogenated to prepare methanol which returns to the boiler for combustion, and the process is repeated, so that the coal consumption of the boiler can be reduced, and the problems of carbon dioxide absorption and storage are solved; part of power generated by the low-temperature nitrogen steam turbine generator unit or the expansion machine generator unit is used for producing hydrogen / oxygen by electrolyzing water, a low-temperature nitrogen freezing treatment device and other auxiliary machines, and the rest part increases the on-grid power, so that the benefits of the coal power unit are greatly improved. The methanol returns to the boiler for combustion, so that the effects of low-load stable combustion of the boiler and rapid load rising of the boiler can be achieved, and the requirements of deep peak regulation and high load change rate of a new-generation coal power unit are met.

The invention relates to a wind power plant active wake flow control method and electronic equipment, and the method comprises the steps: considering the time-varying load influence of a wind turbine generator in the actual operation process, combining the displacement of a long flexible blade in the flapping and shimmy directions, and constructing the dynamic equivalent impeller radius of the long flexible blade under the vertical plane projection; based on the dynamic equivalent impeller radius, a wake flow model is constructed, and the output power of each wind turbine generator is calculated; designing a wind power plant model prediction controller considering flexible characteristic correction by using the output power of each wind turbine generator, and outputting an optimal yaw and variable pitch angle control sequence through rolling optimization in a prediction time domain; and the optimal yaw and variable pitch angle control sequence is applied to the wind turbine generator sets, and dynamic coupling control over all the wind turbine generator sets in the wind power plant is achieved. Compared with the prior art, the system has the advantages that the generating capacity of a large wind power plant is improved, meanwhile, excessive action of the yaw and variable pitch mechanism is avoided, and mechanical fatigue damage can be reduced.

The invention relates to a power grid hour-level green electricity source tracing method and system based on space-time matching. The method comprises the following steps: firstly, collecting power generation data of a power generation end containing a power generation equipment identifier, power generation quantity and a power generation timestamp and power utilization data of a power utilization end containing a user identifier, power utilization quantity and a power utilization timestamp, and preprocessing and integrating into a power generation record and a power utilization record; for each to-be-traced power utilization record, dynamically delimiting a time window according to the power utilization timestamp; all power generation records in the window are inquired and aggregated to calculate the total power generation amount; then, according to the proportion of the generating capacity of each power generation device in the total generating capacity, green electric quantity distributed to the electricity utilization record is calculated, and a distribution relation is recorded to generate a traceability result containing a user identifier, a power generation device identifier, the distributed electric quantity, a time window and matching confidence; and finally, generating an ESG report and an electronic certificate based on the traceability result. Compared with the prior art, the method has the advantages of high traceability precision, high automation degree, high reliability and the like.

The application discloses a wind shear working condition recognition method and system for a wind generating set, the method is that the test data of the wind generating set is obtained through the independent variable pitch equipment of the wind generating set, the bending moment of the wind generating set in the static load coordinate system is calculated, the change rate time sequence is calculated according to the bending moment, the direction and the duration of the change rate in the change rate time sequence are compared with the preset threshold condition, if the direction and the duration of the change rate in the change rate time sequence meet the threshold condition, the wind generating set is recognized to be in the wind shear working condition; the test data can be obtained based on the independent variable pitch equipment, the difference between the wind shear working condition and the normal power generation working condition can be recognized, the load optimization and the clearance optimization of the wind shear working condition can be realized, the power generation capacity of the normal power generation working condition is not affected, and the loss of the power generation capacity of the wind generating set is prevented.

The invention, which relates to the technical field of new energy power generation, discloses a photovoltaic power generation system using a wind power plant ground and a transmission channel, comprising a wind turbine generator group with a reinforced platform, and a photovoltaic array which is distributed below a fan base platform, a wind power plant road and a power transmission corridor and adopts an adjustable double-sided double-glass assembly with an inclination angle of 15-35 degrees. The wind-solar coupling device comprises a mechanical connection mechanism (a fixed support and a maintenance channel) and an electrical coupling unit (a multi-port converter integrated output is connected to a booster station), the dynamic power regulation and control system (comprising an output prediction module and the like) based on wind-solar complementarity, and the current collection circuit with the current-carrying capacity is designed according to the wind-solar maximum superposition power. According to the system, land occupation and power transmission cost are reduced through collaborative layout, output fluctuation ratio is reduced through dynamic regulation and control, power grid acceptance capability is improved, generating capacity is improved through intelligent design, loss is reduced, energy recovery is realized, operation and maintenance cost is reduced, and the problems of resources, stability and efficiency in new energy development are effectively solved.