3109 results about "Solar power" patented technology

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention relates to an intelligent charging method and system based on light and wind complementation and intelligent energy storage. According to the method, dynamic optimized charging is realized through multi-source data collaborative decision, and the method comprises the following steps: firstly, collecting real-time data such as light-wind power generation, energy storage SOC, charging load and commercial power supply, analyzing a wind-solar power generation complementary relationship and calculating a real-time complementary coefficient; a dynamic weight distribution scheme of light wind and commercial power is generated in combination with the commercial power state, and an energy storage charging and discharging instruction is generated by fusing peak and valley electricity prices and energy storage efficiency optimization; and based on the adjusted energy storage output power, comprehensively considering the charging demand and the vehicle battery state, and dynamically distributing the power of the charging pile through the charging priority. By adopting the method, energy complementation, economic scheduling and charging demand management can be combined, the utilization rate of renewable energy sources is effectively improved, and the charging cost is reduced.

The invention discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

The invention belongs to the technical field of wind-solar power generation power monitoring, and particularly relates to a new energy wind-solar power data monitoring system and method. According to the method, power fluctuation characteristics and equipment state correlation indexes are extracted through abnormal value detection and time sequence analysis, so that the precision and efficiency of data processing are improved, and in the aspect of prediction, dynamic comparison between a historical operation data set and the current power fluctuation characteristics is utilized, and environmental parameter change trends are combined. A power prediction curve and a confidence interval are generated, so that the system can recognize potential abnormal operation in advance, decision support time is provided for operation and maintenance personnel, in addition, the system has corresponding fault diagnosis and optimization capabilities, when the prediction deviation degree exceeds a preset threshold value, an early warning mechanism is automatically triggered, a fault source is positioned through a fault diagnosis process, and the fault diagnosis efficiency is improved. And finally, according to a fault diagnosis result, a power prediction curve is corrected and optimized to form a closed-loop optimization mechanism, and the prediction precision and the operation efficiency of the system are continuously improved.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention discloses a slope multi-modal monitoring and early warning system and method based on digital twinning, and relates to the technical field of slope monitoring, and the system comprises a data acquisition module, a data processing module, a digital twinning modeling module, a deformation prediction module, an early warning visualization module and a solar power supply module. According to the invention, slope multi-source monitoring data is obtained through the data acquisition module, and after the slope multi-source monitoring data is preprocessed by the data processing module, a slope digital twinborn model is constructed and calibrated by the digital twinborn modeling module; deformation prediction is realized by combining a space-time prediction model which contains a space correlation and time sequence analysis module and is optimized by multi-scale neighborhood aggregation, a DTW loss function and transfer learning, and a future-state digital twin is generated through an early warning visualization module and risks are visualized. And the solar power supply module is matched to ensure the operation of a scene without an external power supply and the flexible deployment of the assembly connection module, so that a multi-mode, high-precision and sustainable slope monitoring and early warning scheme is formed.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention discloses a wind-solar complementary intelligent charging system and heat management optimization. The system comprises a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, an intelligent control unit and a heat management unit, the wind power generation unit adopts an efficient wind generating set, is provided with an intelligent control system, and can automatically adjust a pitch angle and a yaw angle according to real-time wind speed and wind direction; the photovoltaic power generation unit selects a high-efficiency photovoltaic cell assembly and tracks the position of the sun in real time in combination with an intelligent light following system; and the energy storage unit adopts a high-performance energy storage battery pack and a battery management system. Through accurate prediction and analysis of local wind and light resources, dynamic configuration and cooperative power generation of the wind power generation unit and the photovoltaic power generation unit are realized. Compared with a traditional fixed configuration mode, local wind and light resources can be fully utilized, and the power generation efficiency is improved. Meanwhile, the multi-energy complementary cooperative power generation technology is adopted, organic combination of wind and light power generation and energy storage is achieved, and stable power supply of the system is ensured.

The invention relates to the technical field of comprehensive application of renewable energy sources and hydrogen energy, in particular to a wind-solar-hydrogen multi-energy complementary intelligent scheduling platform. According to the technical scheme, the wind-light-hydrogen multi-energy complementary intelligent scheduling platform comprises a wind-light power generation unit which monitors wind power and photovoltaic power fluctuation in real time; the hybrid energy storage unit comprises a super capacitor and a flywheel for energy storage and respectively responds to millisecond-level and second-level power fluctuations; the hydrogen energy subsystem comprises an electrolytic hydrogen production device, a hydrogen storage tank and a fuel cell and responds to minute-level power scheduling; and the multi-time scale controller is configured to execute millisecond-level, second-level and minute-level cooperative control and dynamically match the wind-light fluctuation and the hydrogen energy response speed. According to the method, through multi-time scale dynamic cooperative control, the matching problem between the wind-solar power generation volatility and the hydrogen energy response lag is effectively solved, and seamless connection between second-level high-frequency fluctuation suppression and minute-level energy scheduling is achieved.

The invention discloses a wind-solar complementary energy storage direct current charging method and system based on liquid cooling heat dissipation, and relates to the technical field of energy storage charging. Comprising the following steps: S1, collecting wind-solar complementary energy storage data in real time, and carrying out data preprocessing; s2, converting wind-solar power generation into direct current, judging the flow direction of wind-solar power generation energy, and performing corresponding wind-solar power generation energy transmission power supply; s3, judging the limit of the output power of the charging pile, and controlling and adjusting the output of the charging pile in stages; s4, quantifying the liquid cooling flow required by heat dissipation, and performing heat source heat dissipation on the energy storage system and the charging pile; and S5, carrying out coordinated regulation and control, strategy optimization and multi-dimensional visualization on the flow direction of wind and light power generation energy, charging pile output and heat source heat dissipation. The problems that an existing charging station is difficult to deploy in remote and power transmission difficult areas, high-power charging heat dissipation and energy efficiency bottlenecks are prominent, and the efficiency of traditional air cooling and alternating current conversion is low, so that efficient, safe and green energy supplementation is difficult to achieve are solved.

The invention provides a power system net load prediction method based on multi-mode decomposition and reconstruction, and aims to solve the problems of violent net load fluctuation, frequent sudden change, difficult prediction and the like under a new energy high-permeability background, and the prediction precision and the trend response capability are improved by fusing a physical mechanism and a deep learning modeling means. Firstly, historical net load data and environmental parameters are collected, and historical wind and light net generating capacity is accurately estimated based on a wind and light generating capacity dynamic coupling calculation method. Afterwards, an improved complete set empirical mode decomposition algorithm is used for conducting multi-mode decomposition on historical net loads, an effective mode function is screened in combination with the energy proportion and correlation, the effective mode function and a residual term jointly reconstruct a net load sequence, and noise interference is eliminated; and after the reconstructed sequence generates a sample through a sliding window, inputting the sample into a hybrid neural network model composed of a convolutional neural network and a long-short-term memory network, and performing training in combination with wind-solar power generation. And finally, multi-step accurate prediction of the future net load is realized.

The invention provides a multi-source energy storage self-power supply method and system for an iron tower, equipment and a storage medium, and belongs to the technical field of iron tower power supply, and the method comprises the steps: inputting historical load data of the iron tower into a time sequence model, and obtaining a load demand of the iron tower; inputting meteorological data of the geographic position of the iron tower into the power prediction model to obtain wind power and photovoltaic power; determining a charging strategy of the energy storage battery based on the load demand, the wind power, the photovoltaic power and the charge state of the energy storage battery; and determining a self-power-supply control strategy according to the charging strategy of the energy storage battery, and selecting any one of a wind-solar power generation device, the energy storage battery or a power grid to supply power to the iron tower based on the self-power-supply control strategy. According to the multi-source energy storage self-powered method and system for the iron tower, the equipment and the storage medium, the overall operation cost of the iron tower is effectively reduced.

The invention discloses a wind and light storage station power reporting and tracking method based on energy storage rapid power response. The method comprises the following steps: acquiring wind and light power probability prediction data, power grid real-time operation state data, station operation state data and power grid examination allowable power deviation data in a current scheduling period; generating a dynamic power reporting interval of the next scheduling period based on the wind-solar power probability prediction data, the power grid real-time operation state data, the station operation state data and the power grid examination allowable power deviation data; and based on the dynamic power reporting interval and the real-time operation data of the wind and light storage station, a flexible control strategy is adopted to control the charging and discharging behaviors of an energy storage system in the station. By generating the dynamic power reporting interval with self-adaptive change and adopting the flexible control strategy, the operation condition of the energy storage system can be obviously optimized, the fluctuation of the wind and light power can be effectively stabilized, and the economy and reliability of the operation of the station can be improved on the premise of ensuring that the wind and light storage station tracks the power grid assessment requirement.

The invention relates to an electricity-hydrogen comprehensive energy system planning method considering multi-time scale uncertainty. The method comprises the following steps: establishing an electricity-hydrogen comprehensive energy system model; based on the electricity-hydrogen comprehensive energy system model, considering the uncertainty of wind and light output and load requirements, and constructing a planning mathematical model of the electricity-hydrogen comprehensive energy system by using a multi-time scale uncertainty modeling method fusing opportunity constraint and stochastic optimization; based on the electricity-hydrogen comprehensive energy system model, establishing an electricity market model as a lower-layer optimization model of a planning mathematical model; and solving the planning mathematical model by adopting a two-stage optimization method to obtain a planning scheme. Compared with the prior art, a multi-time-scale planning mathematical model of the electricity-hydrogen comprehensive energy system is established, and planning of the electricity-hydrogen comprehensive energy system is achieved.

The invention relates to a multi-virtual power plant collaborative transaction method, system and device and a readable medium, and the method comprises the steps: constructing a multi-energy coupling model containing a gas turbine, an energy storage device, biomass power generation and a new energy vehicle, and generating a multi-source data set to quantify the carbon emission cost and charge and discharge constraints; a wind and light output joint probability distribution model is generated based on an F-vine Copula distribution function, and a severe scene set is screened through random sampling and scene closeness evaluation; designing a renewable energy source-load correlation coefficient and a green energy consumption compensation mechanism, and correcting a virtual power plant scheduling objective function; establishing a double-layer dynamic transaction mechanism, and iteratively optimizing an upper-layer dynamic electricity price and a lower-layer multi-source transaction electricity quantity; a multi-stage robust optimization algorithm is adopted to process the uncertainty of wind, light and electric vehicles, and a transaction strategy among multiple virtual power plants in a severe scene is generated, so that the problems of low-carbon excitation deficiency, insufficient multi-source uncertainty coping and the like are solved, and unification of economy, low-carbon property and robustness of collaborative scheduling transaction of the multiple virtual power plants is realized.

The invention belongs to the technical field of intelligent traffic and new energy power supply, particularly relates to a traffic equipment solar power supply fault diagnosis system, and aims to solve the problems that a solar power supply system is not timely in fault diagnosis, low in precision and difficult to distinguish instantaneous interference and continuous faults. The system collects multi-source data through an environment sensing and electrical parameter monitoring module, generates a power deviation sequence and extracts time sequence characteristics by combining dynamic expected power modeling with actual output comparison; the fault identification module adopts a multi-level logic discrimination and 12-hour continuous verification mechanism, accurately identifies photovoltaic panel pollution, storage battery aging, poor line contact and controller faults, and distinguishes instantaneous interference; and the decision alarm module generates graded alarms according to fault types and grades, and realizes accurate positioning and operation and maintenance scheduling in linkage with geographic information. The system also has the functions of internal resistance pulse detection, dual-channel redundancy sampling, adaptive threshold adjustment and model self-learning, and the diagnosis accuracy and the operation and maintenance efficiency are significantly improved.

The invention relates to the technical field of intelligent power grids, in particular to a micro-grid energy storage optimization method and system based on big data analysis, and the method comprises the steps: obtaining the comprehensive prediction power generation through the prediction segmentation time, a wind power generator set and a solar power generator set, and obtaining the analysis power based on the prediction segmentation time and the comprehensive prediction power generation, an energy storage battery parameter set is obtained, if the analysis electric quantity is larger than or equal to a storage electric quantity threshold value, a target storage battery pack is retrieved from the energy storage battery packs through the energy storage battery parameter set, active equalizing charging is conducted on the target storage battery pack, and an updated storage battery pack is obtained; and after it is confirmed that a preset target energy supply battery pack is retrieved in the energy storage battery packs by using the analysis electric quantity and the energy storage battery parameter set, power is supplied by using the target energy supply battery pack, and an updated power supply battery pack is obtained. According to the method, the energy storage of the micro-grid can be accurately and intelligently optimized.

The invention provides a wind and light power station area road planning method and system based on terrain analysis. According to the method, digital elevation model data and vehicle turning radius parameters of a wind and light power station area are processed, layered reconstruction and optimization are carried out, optimized sampling data are generated, and a road network is constructed by using the optimized sampling data; path optimization is further carried out based on the road network data to obtain a path candidate set, and final road path data suitable for the wind and light power station area is generated through an electric field layout and equipment adaptation algorithm, construction adaptability processing and a safe and smooth correction means, so that the road planning precision is improved, the construction adaptability is enhanced, and the construction efficiency is improved. The actual requirements under different topographic conditions are met, the problem that local environment characteristics and operation safety requirements cannot be reasonably integrated in traditional path planning is avoided, and the feasibility of wind and light power station road planning is effectively improved.

The invention discloses a wind and light off-grid hydrogen production two-stage power optimization method considering reserved energy of energy storage, and belongs to the technical field of wind and light off-grid hydrogen production system power optimization, and the technical scheme is that the method comprises the following steps: S1, establishing a wind and light off-grid hydrogen production system mathematical model; s2, inputting minute-level wind and light historical data; s3, formulating a power distribution rule considering deep coupling of an electrolytic bath operation condition and energy storage preparation energy in a first operation stage, and determining hydrogen production power and hybrid energy storage initial distribution power; and S4, in the second operation stage, with the minimum unit hydrogen production cost and the minimum average charge and discharge depth of the storage battery as targets, the hybrid energy storage power is optimized by adopting an improved multi-target particle swarm algorithm. The method has the beneficial effects that the hydrogen production stability and the energy storage and power supply capacity can be improved, and the wind and light abandoning rate and the unit hydrogen production cost can be reduced.

The invention, which relates to the technical field of energy management, discloses a multi-target collaborative optimization prediction system and method for regional-level water-wind-light power generation power, and the system comprises a data collection module which is used for obtaining power generation power data, meteorological environment data and power grid load demand data in real time; the optimization processor is used for receiving the data fed back by the data acquisition module, and constructing a multi-target optimization model based on spatial-temporal feature extraction to generate a plurality of candidate optimization paths; the screening module is used for screening the candidate paths through a dynamic weight distribution algorithm and screening out an optimal collaborative optimization path; and the dynamic feedback module is used for carrying out online correction on the optimal collaborative optimization path in the real-time optimization process according to the power grid frequency deviation, the energy storage charge state and the meteorological abrupt change signal. The optimization processor constructs the multi-target optimization model based on the spatial-temporal characteristics, generates a plurality of candidate optimization paths and screens out the optimal path, and compared with a traditional single energy prediction and simple scheduling mode, the precision of power generation power prediction is improved.

The invention discloses a water-wind-light combined intelligent control system based on multi-energy coordinated control, and belongs to the technical field of multi-energy coordinated control, and the system comprises the steps: firstly collecting environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, and carrying out the preprocessing of the environment and operation parameters to form a structured data set with a space-time label and a dynamic sampling identifier; performing space-time correlation analysis on the data set, constructing a wind power fluctuation and hydropower regulation margin real-time response matrix and a photovoltaic output attenuation and hydropower storage capacity compensation time sequence correlation model, and quantifying the multi-energy dynamic complementation degree and grading the data credibility through a two-dimensional coupling algorithm; then combining with a historical meteorological scene association rule base, constructing a water-wind-light combined power supply capability prediction model containing an energy characteristic mutual compensation coefficient, and generating a hierarchical regulation and control strategy with a space-time priority; and finally, executing a regulation and control strategy to complete parameter adjustment, continuously monitoring through closed-loop feedback, and triggering multi-energy collaborative complementary regulation when the single energy regulation capability is saturated, thereby realizing water-wind-light efficient collaborative control.

A landing pad receives and stores packages delivered from an aerial vehicle or awaiting pickup from an aerial vehicle. The landing pad can be placed outside of a window and can contain a transmitter for sending out an identification signal via radio frequency to aid aerial vehicles in finding the landing pad. The landing pad contains a landing platform with a trapdoor that leads to a storage compartment. The trapdoor can be configured to only open when it receives a signal from an authorized aerial vehicle. The storage compartment can be accessed via a storage compartment door which can contain a locking mechanism. The storage compartment can be climate controlled. The landing pad can also have a transmitter that emits sounds to discourage animals from nesting on or near the landing pad. The landing pad can also include a solar power generator as a source of electrical energy.

The invention discloses an island energy system opportunity constraint weekly optimization scheduling method and system, and the method comprises the steps: converting the opportunity constraint of the actual output of wind and light into a deterministic inequality constraint based on the prediction error probability distribution characteristics of wind power and photovoltaic; according to the obtained wind and light output constraint, a dynamic operation model is established, and waste heat output of the hydrogen fuel cell is coupled with an absorption refrigerator; with the purpose of minimizing the total operation cost of the system, combining the obtained deterministic inequality constraint and the dynamic operation model, defining the power balance constraint of electricity / heat / cold / fresh water, the hydrogen storage tank capacity constraint and the equipment climbing rate constraint, and constructing an optimization model; and solving the optimization model by adopting mixed integer programming to obtain an optimization scheduling scheme. The requirements of electricity, cold, heat and fresh water of an ocean island energy system are met, and meanwhile energy storage and cross-period utilization during wind and light resource enrichment are achieved.

The invention relates to the technical field of new energy power generation prediction, and discloses a t-SNE visualization and depth time sequence attention model-based wind and light power prediction method and system, and the method comprises the steps: obtaining historical power data and corresponding historical meteorological data of a wind power station and a photovoltaic station, and constructing a historical data set; inputting the preprocessed high-dimensional meteorological data into a t-SNE dimension reduction module, and mapping the high-dimensional data to a low-dimensional space through symmetric joint probability density calculation to obtain low-dimensional visual data distribution; the low-dimensional data are input into a parallel model composed of a TCN-SENet branch and a BiGRU-GlobalAttention branch, and space-time local features and global time sequence features are extracted respectively; and fusing the feature vectors output by the two branches, generating a wind-solar power prediction result through a full-connection layer, and performing evaluation. According to the method, the limitation of traditional single energy independent modeling is broken through, the adaptability of the model to a complex power generation mode of a distributed station is optimized, and the precision and generalization ability of multi-energy joint prediction are remarkably improved.

The invention relates to the technical field of water electrolysis hydrogen production, and discloses an electrolytic bath outlet temperature control method and system, a medium and a program product, a flow control valve is arranged at an inlet of an electrolytic bath, the electrolyte input flow is converted into a core regulation variable directly influencing the outlet temperature, and active control over the outlet temperature is achieved. Furthermore, the flow control valve is directly connected with the control device, the opening degree signal can be received in real time, the flow can be adjusted, it is ensured that the temperature control instruction can be rapidly converted into the actual flow change, and the response speed to power fluctuation is increased. Furthermore, through a real-time operation state parameter set and a real-time temperature monitoring parameter set, it is ensured that a control signal is generated based on a real working condition, and the temperature control stability under fluctuating power is improved. Furthermore, through the synergistic effect of an electrolyte flow feedforward control signal and an electrolyte flow rear-end control signal, frequent current change caused by wind-solar power fluctuation can be effectively handled, and the fluctuation amplitude of the outlet temperature is reduced.

The invention provides a cascade reservoir operation risk and benefit evaluation method considering prediction uncertainty, and the evaluation method comprises the steps: building a day-ahead wind-light-water optimal complementary model, and employing a two-stage robust method to process the power generation amount deviation generated by the wind-light uncertainty; the method comprises the following steps: identifying the worst condition of photovoltaic power output which causes maximum water consumption and power imbalance to obtain a day-ahead total power generation plan of the wind-solar hybrid system capable of realizing maximum resource utilization rate; according to the evaluation method, a benefit and risk evaluation model based on power generation complementary benefit, power generation stability, power supply reliability and water level fluctuation is further provided, the risk of complementary operation of the reservoir system is quantified through indexes, the risk and benefit of cascade reservoir operation can be reasonably evaluated and balanced on the basis of considering the uncertainty of wind and light power generation, and the method is suitable for popularization and application. And the overall efficiency and reliability of the power system are improved.

The invention discloses a monitoring device and a monitoring method for construction organization, belongs to the technical field of intelligent construction safety monitoring, and mainly solves the problems of monitoring blind areas, insufficient fixed visual angle coverage and difficulty in field long-term power supply caused by dynamic shelters in the construction process. According to the technical scheme, a camera is fixed to a double-shaft electric holder with a horizontal rotating and pitching adjusting mechanism, a processor, a communication unit and a battery are arranged in a base, a solar panel is arranged on the top of the base, and an electric lifting column is connected with the base and the holder; the environment sensor group is mounted on the camera; the solar panel is connected with the battery and supplies power to the whole device; the processor calculates a target monitoring path for eliminating a blind area in combination with a preset three-dimensional coordinate of a key area, generates a control signal to drive the holder and the lifting column to move, and dynamically adjusts the path execution frequency and the movement speed of each mechanism according to the electric quantity of a battery and the solar power. The device is mainly used for dynamic dead-corner-free safety monitoring of key areas of a building construction site.

The invention discloses a renewable energy scheduling control method based on electricity-carbon-green evidence market coupling, and relates to the technical field of renewable energy control, and the method comprises the following steps: predicting wind and light output fluctuation intervals based on a neural network, and classifying the wind and light output fluctuation intervals; constructing a first simulator based on chaotic mapping, and constructing a second simulator based on quantum machine learning; controlling to switch the first simulator and the second simulator according to a classification result, and outputting a wind-light output predicted value; and controlling energy storage output according to the wind-solar output predicted value and a preset electricity-carbon interaction model. The method is used for solving the problem of insufficient simulator adaptability in different wind and light output fluctuation scenes and insufficient energy storage control capability in an electricity, carbon and green evidence cooperative scene.