2176 results about "Generating capacity" patented technology

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

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 intelligent regulation and control method and system for a photovoltaic energy storage air conditioner. The method comprises the steps that photovoltaic related data, energy storage related data and air conditioner operation related data are collected; inputting the photovoltaic related data into the photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction result; inputting the air conditioner operation related data into the air conditioner load prediction model to obtain an air conditioner load prediction result; inputting the photovoltaic generating capacity prediction result, the air conditioning load prediction result, the energy storage related data and the photovoltaic related data into an energy storage optimization scheduling model to obtain an energy storage charging and discharging decision; constructing a power supply strategy core rule base based on the energy storage related data, the electricity price peak time period and output results of the photovoltaic power generation prediction model and the air conditioner load prediction model; and optimizing an output result of the energy storage optimization scheduling model based on the power supply strategy core rule base. The method has the outstanding advantages of high reliability, high photovoltaic utilization rate, low dependence degree on a power grid and the like.

The invention relates to the technical field of power electronics, in particular to a photovoltaic off-grid inverter control method and system, and the method comprises the following steps: applying a test signal to identify an output interaction characteristic parameter, predicting the power generation power through combining with weather and attenuation, calculating the power fluctuation, and adjusting a current control parameter set. And calculating voltage and reactive power compensation parameters to generate an inverter control instruction, and combining a temperature load prediction trend to adjust a power allowable value to obtain a predictive power calibration coefficient. According to the invention, by analyzing the output interaction characteristics, enhancing the perception capability of the inverter to the actual operation environment, combining with the prediction of the photovoltaic power generation amount, realizing the prediction and influence evaluation of the power fluctuation, improving the adaptive adjustment capability of the current control parameters, improving the output voltage and reactive power control precision by utilizing the interaction characteristics, and optimizing the electric energy quality; and through thermal management of multi-point temperature and load prediction, the operation safety is improved, the service life of equipment is prolonged, and the operation performance of the photovoltaic off-grid system in a complex environment is improved.

The invention discloses a micro-grid intelligent economic regulation and control system and method based on carbon emission optimization, and relates to the technical field of economic regulation and control. The method comprises the following steps: deploying an Internet of Things sensor to collect photovoltaic generating capacity, energy storage SOC, load demand and power grid carbon intensity data in real time, and transmitting the data to an edge computing node through a 5G / optical fiber hybrid communication network; preprocessing the data by adopting wavelet transform, and establishing a time sequence prediction model of photovoltaic output / load demand; constructing a dynamic carbon flow tracking matrix; performing decision optimization according to the output dynamic carbon emission spectrum and the power carbon flow traceability data; and the edge node issues a control instruction through a Modbus-TCP protocol to carry out economic regulation and control. According to the method, multi-source data are collected in real time, a minute-level carbon emission equation is established based on a dynamic carbon flow tracking model, dynamically-changed energy carbon emission factors and line transmission loss are fused, and the micro-grid carbon footprint is accurately quantified.

The invention provides a new energy electric power intelligent allocation method, which comprises the following steps: according to historical electricity consumption data of a campus and an industry-university-research area, extracting an electricity consumption load change trend of a teaching area and the industry-university-research area, analyzing an electricity consumption period difference between a holiday and a school starting season, and determining an electricity consumption complementation time period; the method comprises the following steps: deploying a photovoltaic array dynamic strategy based on power utilization plan data of a campus and an industry-university-research area, calculating a target deployment position of a photovoltaic array in different time periods by combining the moving capability and the power generation efficiency of the photovoltaic array, and generating a dynamic deployment scheme of the photovoltaic array; and according to the actual execution data of the photovoltaic array dynamic deployment scheme, obtaining a comparison result of the photovoltaic power generation capacity and the power demand, analyzing the effect of power cooperation and mutual assistance, optimizing a photovoltaic array deployment strategy and an off-peak production mechanism, and generating a school-enterprise linkage power supply scheme.

The invention discloses an intelligent scheduling method for video equipment based on solar power supply, and relates to the technical field of energy management and video. Through multi-dimensional data acquisition, intelligent prediction and dynamic scheduling technologies, the energy management challenge faced by solar power supply video equipment in remote areas is effectively solved, the power generation amount, equipment power consumption and environmental change data are acquired in real time by using an Internet of Things sensor, and the energy management efficiency is improved by combining time sequence analysis and a machine learning algorithm. The power generation amount and power consumption fluctuation are accurately predicted, an energy supply and demand matching model is established based on a dynamic planning algorithm, a power distribution strategy is optimized, operation of high-priority equipment is guaranteed preferentially when the power generation amount is insufficient, meanwhile, the working mode of the equipment is dynamically adjusted through a reinforcement learning algorithm, and energy consumption optimization and self-adaptive matching are achieved. The energy utilization efficiency and the system stability are improved, the adaptive capacity to dynamic environment changes is enhanced, and continuous and stable operation of video equipment under complex conditions is ensured.

The invention provides a photovoltaic system-based generating capacity loss intelligent evaluation method, which comprises the following steps of: 1, acquiring optical parameters in real time, and synchronously acquiring predicted irradiation data of a meteorological station; step 2, performing feature extraction on the current and voltage curve, identifying abnormal inflection points in the curve, dividing mismatch types, determining theoretical output power of each group of strings, and generating a future theoretical generating capacity baseline; 3, obtaining a group cascade stage power loss rate, and determining a current loss reason in combination with a mismatch type; 4, identifying a current system-level fault area; and step 5, based on a historical fault case library, extracting a group cascade power loss rate, a mismatch type and spatial distribution characteristics of a system-level fault area, matching a loss mode of a current system with a historical fault type, generating an evaluation report including a loss quantized value, a fault position and a repair priority, and carrying out fault recovery. And an optimization operation and maintenance instruction including a personnel scheduling scheme and a spare part demand list is generated. And the operation efficiency and the maintenance effect of the photovoltaic system are improved.

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 relates to the technical field of emergency power supply, and provides an emergency power supply method and system for a household energy storage power supply. And performing dynamic health assessment through the multi-dimensional state matrix of the target battery pack to obtain an SOH value and an SOC value of the target battery pack, and performing dynamic energy distribution according to the SOH value, the SOC value and a load demand to obtain a multi-target charging and discharging optimization strategy. Inverter output waveform synthesis and dynamic harmonic compensation are carried out according to a multi-target charging and discharging optimization strategy to generate pure sine wave alternating current, and a battery state, power grid power failure risk and photovoltaic power generation amount prediction and adjustment time-sharing energy storage dynamic adjustment instruction is carried out in combination with environment prediction information; and performing multi-energy input cooperative control distribution according to the time-sharing energy storage dynamic adjustment instruction and the real-time energy input state to generate a multi-source power distribution instruction. According to the invention, the safety, reliability and economy of the household energy storage system in the sudden power failure scene are improved through the combination of the technologies of risk prediction, cooperative control and the like.

The invention discloses a regional carbon emission responsibility determination method and device based on a carbon emission factor, terminal equipment and a storage medium. The method comprises the following steps: acquiring the electric power type, the called-in electric power total amount, the called-out electric power total amount, a first carbon emission factor, the net consumption data of fossil fuel, fuel characteristic parameters, the fuel type number, the transaction electric quantity, the local power generation amount and the non-green power utilization electric quantity of a target area; calculating the total direct carbon emission according to the net consumption data, the fuel characteristic parameters and the fuel type quantity; calculating the total indirect carbon emission according to the electric power type, the total called-in electric power, the total called-out electric power and the first carbon emission factor; calculating a regional power average carbon emission factor of the target region according to the transaction power quantity, the local power generation quantity, the total direct carbon emission quantity and the total indirect carbon emission quantity; and according to the regional power average carbon emission factor and the non-green power consumption quantity, the carbon emission responsibility of the target region is checked. According to the invention, accurate accounting of electric power carbon emission is realized.

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 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 an intelligent prediction and scheduling management system based on hazardous waste incineration waste heat recovery power generation, relates to the technical field of hazardous waste incineration waste heat recovery power generation, and aims to monitor key parameters such as temperature, pressure, flow and flue gas components in the hazardous waste incineration process in real time, and determine the hazardous waste incineration waste heat recovery power generation through data processing and feature extraction. And the hazardous waste incineration heat value, the waste heat boiler steam production amount and the power generation amount are accurately predicted. Meanwhile, the system combines power grid load change and price fluctuation, realizes intelligent regulation and control of equipment operation and maximization of waste heat recovery efficiency and generating capacity through an optimization scheduling algorithm, can improve energy recovery efficiency to the maximum extent, reduces energy waste, improves economic benefits and environmental sustainability of the system, and has wide application prospects. The power generation benefit can be improved in the field of hazardous waste incineration, and reference can be provided for other industrial waste heat recovery power generation systems.

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 electric power systems, in particular to an electric power spot market risk prevention and control method and system considering market subject behaviors. Acquiring operation data of generating capacity, electricity demand, market price and power grid state of the electric power spot market; on the basis of the acquired operation data, a coupling degree evaluation index and a stability evaluation index are introduced to carry out prediction and anomaly detection on a market trend, and influence factors of connection of a large-scale generator set and a small-scale power grid are particularly concerned; analyzing a market subject behavior pattern through transaction frequency, price volatility and market participation degree characteristic quantitative indexes, and identifying market manipulation and violation operation risks; market dynamics are monitored in real time, a risk threshold value is set, and an alarm is triggered when a risk index exceeds a set standard; early warning information of different levels is automatically sent out according to the risk degree, and corresponding countermeasures and suggestions are provided; and a supervision mechanism is helped to make a quick response through a visual interface.

The invention relates to the technical field of energy management and control, in particular to a micro-grid energy management and control system based on energy storage and distributed photovoltaic, and the system comprises a load prediction scheduling module which collects real-time grid data, analyzes the current load and photovoltaic generating capacity, carries out the time sequence analysis, and generates real-time load prediction data. According to the invention, in the load prediction, the real-time load prediction data generated based on time sequence analysis is combined with the photovoltaic power generation fluctuation, the charging and discharging strategy of the energy storage equipment is dynamically corrected, and the energy supply mismatching caused by the load fluctuation is reduced. According to the causal inference method, a multi-layer causal chain is constructed, key influence factors in photovoltaic operation are identified in combination with real-time load prediction data, real-time adjustment of energy distribution is realized through feedforward correction of fault early warning signals, and equipment overload or energy waste caused by imbalance of energy flow directions is avoided.

The invention relates to the technical field of wind-solar hybrid power generation, and discloses a wind-solar complementary energy storage power station intelligent scheduling method and device based on time sequence analysis, and the method comprises the steps: judging whether a target wind-solar complementary energy storage power station is in a free power generation mode or not based on the operation state of the wind-solar complementary energy storage power station; if the target wind-solar complementary energy storage power station is in the free power generation mode, collecting wind energy output data and photovoltaic output data in a plurality of period windows; performing time sequence analysis on the wind energy output data and the photovoltaic output data in the plurality of period windows, and determining the time sequence similarity of the output data in the adjacent period windows; dynamically adjusting the power generation power of the power station based on the time sequence similarity of the output data in the adjacent period windows to obtain the maximum power generation capacity of the power station; and obtaining a preset adjustment factor, and carrying out generation power allocation on the target wind-solar complementary energy storage power station based on the maximum power generation amount and the preset adjustment factor. According to the invention, efficient power generation of the target wind-solar complementary energy storage power station in different operation states is ensured.

The invention relates to the technical field of energy storage load scheduling, in particular to an energy storage load scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining historical electricity price, load power and renewable energy power generation power data of each time period before the current time as input variables, and carrying out the normalization processing to obtain input data; inputting the input data into an LSTM-Transformer prediction model, and predicting to obtain the power grid electricity price, the load demand and the renewable energy power generation amount in a future time period; establishing a multi-objective optimization model including electric charge cost, operating profit and energy storage life; substituting the prediction data into the multi-objective optimization model, and solving a Pareto frontier based on a multi-objective optimization algorithm to obtain a non-inferior solution set satisfying the multi-objective optimization model; and taking each solution in the non-inferior solution set as input, and screening the solution with the maximum Sharpley value as an optimal energy storage charging and discharging adjustment strategy. By accurately controlling the working state of the energy storage system, the interactive operation mode of the energy storage system and the power grid is optimized.

The invention discloses a cooperative photovoltaic power generation grid-connected control method, system and device and a medium, and relates to the field of photovoltaic power generation. The method comprises the following steps: acquiring environmental parameters and historical power generation data of each photovoltaic power station in a cluster, and predicting an optimal power generation state of each photovoltaic power station in future preset time according to the environmental parameters and the historical power generation data; determining an output power target value and a voltage target value of the target photovoltaic power station according to the optimal power generation state; adjusting a maximum power point tracking control parameter of the target photovoltaic power station according to the output power target value and the voltage target value, and controlling the target photovoltaic power station to generate power according to the maximum power point tracking control parameter; predicting the power demand in the future preset time according to the current demand of the power grid, calculating the difference between the overall generating capacity of the cluster and the power demand, and controlling the energy storage system to output or store electric energy according to the difference. By implementing the technical scheme provided by the invention, the effect of improving the photovoltaic power generation efficiency is achieved.

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.

A method of power management of a hybrid power plant includes at least one type of renewable power generation equipment and an energy storage system, including: before a first dispatching time range: using first forecasts of energy production capability and energy price, and in particular hybrid plant status and plant parameters, to derive a first power generation schedule defining power generation in the first dispatching time range; at a dispatch point in time within the first dispatching time range: using second forecasts of energy production capability and energy price, and in particular plant status and plant parameters, to derive a second power generation schedule defining power generation in a second dispatching time range; and calculating an optimal power injection value for the dispatch point in time based on the first power generation schedule and the second power generation schedule taking into account at least one constraint.

The invention provides a wake flow control method and system for a wind turbine generator of a wind power plant. The method comprises the steps that wind field data of a wind power plant and unit operation state data of a wind turbine unit are acquired; according to the wind field data, wake flow distribution characteristics are determined; estimating wake flow characteristic parameters according to the wake flow distribution characteristics and the unit operation state data; according to the wake flow characteristic parameters, a wake flow control instruction is generated; according to the wake flow control instruction, the yaw rate and the pitch angle of an upstream unit are controlled, so that wake flow deviates from a downstream unit, and the operation state of a wind turbine generator in a wind power plant is dynamically adjusted through circulation. According to the scheme, through wake flow control, on the premise of guaranteeing optimization of the average operation characteristics of the wind power plant, the power generation capacity is improved, and safe and stable operation of the unit is guaranteed according to operation data of all sensors and units in the wind power plant.

A method including the following steps: computing availability of at least one renewable electrical energy production device that delivers electricity to at least one household associated therewith to deliver electricity to an electric grid; computing an electricity usage plan for the predetermined period of time; optimizing the electricity usage plan so that: the electricity to be delivered is to be delivered to the electric grid at one or more different time periods at least when an amount of the possible electric power generation corresponding to non-renewable electrical energy generators is greater than at other time periods within the predetermined period of time; and during a predetermined duration after the time period or periods when the electricity is delivered to the electric grid, the electricity delivered by the at least one renewable electrical energy production device for consumption by the respective household is lower than the computed availability during the predetermined duration.

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 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 relates to the field of energy management, and discloses a light storage and charging integrated energy management system, which comprises a central energy management platform used for monitoring and scheduling energy flow among photovoltaic power generation, energy storage equipment and a charging pile in real time, and dynamically adjusting the energy flow according to the change of the power grid load, the state of the energy storage equipment and the photovoltaic power generation amount; the photovoltaic power generation monitoring and scheduling module communicates with the central platform and is used for acquiring the power generation amount data of the photovoltaic module and adjusting the use priority of photovoltaic power generation according to the load condition of the power grid; and an energy storage management and scheduling module. Through cooperative work of the central energy management platform, photovoltaic power generation is preferentially supplied to the energy storage equipment and the charging pile, electric power waste is avoided, maximum utilization is achieved through combined action of the photovoltaic power generation and the energy storage equipment when a power grid is in a low ebb, the energy storage equipment discharges to relieve the pressure of the power grid when the power grid is in a peak period, and therefore the energy utilization efficiency of the system is remarkably improved.

The invention discloses a fan working performance monitoring method and device, and relates to the technical field of wind power generation, and the method comprises the steps: collecting environment data, equipment working state data and equipment electrical data, and carrying out the preprocessing; performing multi-source data integration on the preprocessed equipment working state data and equipment electrical data to obtain a data set; extracting an input feature vector from the data set; constructing a fault recognition model, and obtaining fault state data of the equipment according to the input feature vector; constructing a health degree evaluation model, wherein the model is used for evaluating the operation condition of the equipment according to the fault state data; if the operation condition of the equipment is good, the current generating capacity of the equipment is judged by combining the collected data, and when the current generating capacity is lower than the average value of the historical generating capacity, the propeller pitch angle is dynamically adjusted until the wind energy utilization rate of the equipment reaches a threshold value; if the operation condition of the equipment has a problem, early warning is carried out through the sound-light alarm unit; the method is used for solving the problem of inaccurate judgment on the working performance of the fan traditionally.