2867 results about "Generating capacity" patented technology

The invention relates to an optical storage cooperative control optimization method, electronic equipment and a medium. The method comprises the steps of collecting historical energy consumption and photovoltaic data of a park, constructing a photovoltaic electronic model based on the data, performing day-ahead prediction by adopting CNN-BiGRU, and generating a load power and photovoltaic generation power sequence; calculating the electricity consumption and the photovoltaic generating capacity of each time period; setting the capacity of an energy storage module, and calculating the energy storage charging and discharging capacity based on an SOC formula; according to the electricity consumption, the generating capacity and the energy storage capacity, determining a peak section discharge demand; photovoltaic fluctuation constraint is combined, and energy storage charging and discharging power is adjusted to stabilize fluctuation; optimizing load power utilization, photovoltaic power generation and energy storage power scheduling based on the power balance relation; and constructing a revenue maximization objective function, establishing a light storage collaborative optimization model in combination with constraints, and solving an optimal operation scheme of a prediction day. According to the method, photovoltaic power generation errors are reduced, power output is balanced, power grid requirements are met, the photovoltaic consumption rate is improved, healthy operation of the system is guaranteed, and operation benefits are improved.

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.

The present disclosure provides a real-time carbon emission accounting method and apparatus considering green electricity. The real-time carbon emission accounting method includes: periodically obtaining influencing factor data, where the influencing factor data includes at least green electricity in net electricity exported from another country to China, and green electricity in an annual total generating capacity within a geographical scope of China's power grid; inputting the influencing factor data into a preset real-time carbon emission factor prediction model for processing to obtain an updated real-time carbon emission factor; obtaining activity data of a to-be-accounted enterprise; and calculating a carbon emission accounting result based on the activity data and the updated real-time carbon emission factor. This solution utilizes the real-time carbon emission factor prediction model to predict and update a carbon emission factor to avoid a current existing hysteresis nature of the carbon emission factor, and considers the green electricity when predicting and updating the carbon emission factor, such that the carbon emission accounting result can accurately reflect an indirect carbon emission generated by use of electricity under the existing energy structure.

The invention discloses an energy storage control and adjustment method and system for distributed photovoltaic consumption, and relates to the technical field of renewable energy sources, and the method comprises the steps: obtaining illumination intensity, temperature data and historical load data, and predicting the future photovoltaic power generation amount and load demand changes; analyzing the voltage fluctuation of the power grid and the charging demand of the energy storage system based on the prediction results of the photovoltaic generating capacity and the load demand; according to the health state of the energy storage battery, optimizing the charging and discharging depth, and outputting an energy storage adjustment plan; dynamically optimizing the charging and discharging plan and reactive power regulation of the energy storage system by using a multi-objective optimization algorithm, and generating an optimal reactive power regulation instruction; according to the reactive power adjustment instruction, performing transformer area self-adaptive cooperative control to obtain a cooperative control result; according to the invention, through an area adaptive cooperative control mechanism, the reactive power regulation capability of the adjacent areas can be automatically coordinated when the regulation capability of the local energy storage system is insufficient, and the stability of the power grid and the utilization efficiency of the energy storage system are further enhanced.

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 relates to the technical field of dynamic scheduling, in particular to a dynamic optimization scheduling algorithm for a photovoltaic energy storage system, which is characterized in that based on a working environment of the photovoltaic energy storage system, environment illumination intensity data is acquired in real time, average power generation under the same illumination intensity is extracted, and weather conditions in a future time period of a current region are received through weather forecast data; and predicting the generating capacity in a future period to obtain generating capacity evaluation information. According to the invention, by capturing the environment illumination condition in real time and combining the historical power generation data, the future power generation condition of the photovoltaic system is effectively predicted, the load change of the power grid is monitored in real time, and the influence brought by specific activities is considered, so that timely response can be made when the demand of the power grid fluctuates, and the supply and scheduling of electric energy are optimized; according to the invention, continuous monitoring and abnormity processing are carried out on the power generation equipment, and the abnormal condition of the equipment is handled by correcting the discharge capacity in real time, so that the risk in operation of the energy storage system is reduced, and solid technical support is provided for stable operation of a power grid.

The invention relates to a hydropower station safety comprehensive management system based on a digital twinborn scene, which designs a hydropower station data resource coding system, establishes a hydropower engineering digital twinborn base, integrates all related geospatial data, basic data, monitoring data and business management data of a hydropower station, and constructs the hydropower station digital twinborn scene. Predicting displacement and deformation conditions of a building structure of the hydropower station and a change trend of important facility and equipment state indexes of the hydropower station, and predicting and pre-judging safety risks therein; the reservoir flow of the hydropower station, the upstream and downstream river section water level, the flow change and the power generation output and the power generation capacity of the hydropower station are forecasted or predicted, corresponding plans are provided for flood early warning or meteorological disaster early warning or water level over-limit early warning, and key links of the plans are simulated in combination with a simulation engine; and performing simulation calculation on different power generation and reservoir scheduling strategies to obtain an optimal scheduling scheme. According to the invention, the intelligent management of hydropower station engineering safety, scheduling safety and power generation safety is realized.

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 a multi-machine cooperative yaw control method and device considering a dynamic wind direction and a medium. The multi-machine cooperative yaw control method comprises the steps that S1, the dynamic relation between the wind direction and a wake flow area is established based on a Gaussian-curl hybrid model; s2, constructing a single-machine yaw optimization model based on model predictive control, optimizing single-machine yaw by taking maximum generating capacity of a wind power plant and reduction of fatigue load as optimization objectives, and obtaining a single-machine optimal yaw angle through rolling optimization and feedback correction; and S3, a serial optimization method is adopted to carry out multi-round iterative optimization on one fan by one fan, and an optimal multi-fan cooperative yaw control result is obtained. Compared with the prior art, the method has the advantages that higher total generating capacity and more balanced fatigue load can be realized, so that the whole wind power plant reaches the optimal state of power and load in a large range.

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 power generation performance evaluation method for an offshore floating photovoltaic power generation system, and the method comprises the steps: considering the influence of offshore wind wave fluctuation, researching the inclination angle change characteristics of a photovoltaic module installed on a floating body structure under different sea conditions, and building a single-sided and double-sided photovoltaic module dynamic and static irradiance model considering the influence of sea wave motion; the invention provides a method for generating a scene with uniform and non-uniform irradiance distribution of an offshore floating type photovoltaic array. The irradiance distribution model and the electrical model of the offshore floating photovoltaic array are combined to form a method for simulating the maximum output power Pmax change characteristic of the offshore floating photovoltaic array under the influence of sea wave operation; and evaluating the annual energy output in the target sea area by combining the wind wave data and irradiance distribution in the target sea area and the maximum output power Pmax change characteristics of the floating photovoltaic array. Compared with an existing photovoltaic power generation performance evaluation method, the influence of sea wave fluctuation on the power generation characteristics of the photovoltaic array is considered, and the method is suitable for an offshore floating type photovoltaic power generation scene.

The invention discloses a new energy charging pile charging control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting user historical charging data, power grid load data, fixed electricity price information and weather forecast data in real time, and recognizing user charging habits and demands; generating a personalized charging scheduling strategy according to the charging habit of the user and the real-time power grid state; the availability of various energy sources is monitored in real time, the generating capacity and the cost of each energy source are evaluated, the priority of energy source selection is adjusted by using a personalized charging scheduling strategy, and an optimal charging mode is automatically selected; executing charging operation based on the charging mode selected by the user, and performing adjustment according to the power grid load and the energy consumption condition; the availability of various energy sources is monitored in real time, the generating capacity and the cost are evaluated, and the priority of the energy sources is dynamically adjusted, so that the response capability of the charging pile to environment change is improved, the charging cost of a user can be reduced, and renewable energy sources are fully utilized.

The invention relates to a TCN-LSTM wind power generation capacity prediction method combining time-frequency conjoint analysis and a multi-head self-attention mechanism. The TCN-LSTM wind power generation capacity prediction method comprises the following steps: preprocessing original data of wind power generation; frequency domain features of the wind power generation data are extracted through wavelet transform, adaptive weighted fusion is carried out on the frequency domain features and time domain features, and a time-frequency conjoint analysis module is constructed; a TCN-LSTM combined network of a time convolution network TCN and a long short-term memory network LSTM is constructed; a multi-head self-attention mechanism is introduced into the TCN-LSTM combination network, and a TCN-LSTM wind energy generating capacity prediction model combining time-frequency analysis and the attention mechanism is generated; training the model by using the wind power generation data set; the trained prediction performance is evaluated; and performing wind power generation prediction by using the final wind power generation prediction model. According to the method, the accuracy of wind power generation prediction and the calculation efficiency of the model are remarkably improved, and the precision of wind power prediction is improved.

The invention discloses a wind power plant dual-mode dynamic active wake flow control method and system and a medium, and the method comprises the steps: taking the maximum power output, power balance and fatigue load stabilization as optimization targets for solving and obtaining an optimal control instruction if a current control mode is a power efficiency improvement mode; otherwise, taking the maximum power output as an optimization target to solve and obtain an optimal control instruction; issuing the optimal control instruction to the virtual wind power plant, and delaying to obtain a delayed control instruction; if the mode is a power efficiency increasing mode, directly issuing to a real wind power plant; and if the mode is a power tracking mode, solving a final optimal control instruction by taking power reference tracking, control reference tracking and fatigue load stabilization as optimization targets, and issuing the final optimal control instruction to the real wind power plant. The method aims at weakening the influence of the wake effect in the wind power plant through dynamic active wake control, improving the generating capacity, maintaining the fatigue load balance and bringing higher economic benefits to operators.

The invention discloses an intelligent distributed photovoltaic power station optimal scheduling and operation and maintenance management method and system, and relates to the technical field of optimal scheduling. Historical power generation data, real-time meteorological data and future weather forecast of a photovoltaic power station are acquired; according to the historical power generation data, the real-time meteorological data and the future weather forecast, inputting the historical power generation data, the real-time meteorological data and the future weather forecast into a pre-trained power generation prediction model to obtain a photovoltaic power generation prediction value in a future time period; and obtaining the power grid demand power of the photovoltaic power station, and planning the charging and discharging strategy of the energy storage system according to the absolute value of the difference between the power grid demand power and the photovoltaic power generation prediction value. The difference between the power grid demand power and the photovoltaic generating capacity prediction value is analyzed, the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system are adjusted according to the difference, the completion degree of the charging and discharging strategy is judged through the charging and discharging time of the energy storage system, the adjusting function of the energy storage system is fully played, and the energy storage efficiency is improved. And the reliability and continuity of power supply are improved.

The invention relates to the technical field of wind energy management, and discloses a fine energy management method and system for a wind power plant, and the method comprises the following steps: S1, obtaining multi-source data, including real-time operation data, meteorological data and power grid load data of the wind power plant; s2, preprocessing the acquired multi-source data, including data cleaning, denoising, interpolation filling and standardization processing; s3, the generating capacity of the wind power plant is predicted based on a deep learning model, and the deep learning model is a hybrid model and comprises a long and short-term memory network and a convolutional neural network; and S4, using the generating capacity data output by the prediction model, and combining with the power grid load demand. Through deep learning, reinforcement learning and machine learning technologies, the power generation prediction precision of the wind power plant is improved, the scheduling and energy storage management strategy is optimized, and fault prediction and health monitoring of the wind turbine generator are realized, so that the operation efficiency and reliability of the wind power plant are improved.

The invention provides an energy management system based on a cloud and edge integrated optical storage system. The energy management system comprises a cloud module and an edge module, the EMS controller is used for collecting information of the optical storage equipment module and the load module and uniformly sending the collected data to the cloud module; the cloud module predicts future photovoltaic power generation power / electric quantity through a photovoltaic power generation prediction model by receiving data acquired by the EMS controller; predicting a future load through the load prediction model; analyzing peak and valley time periods of the electricity price through the electricity price management model, and sending prediction data to the edge module; and the edge module carries out energy management through the control logic of energy management according to the battery SOC, the chargeable amount, the photovoltaic predicted power and the generating capacity, and superposition peak-valley arbitrage and safe anti-countercurrent logic. Through the arrangement, the whole energy management system has high intelligence and automation levels, energy distribution and use are optimized, and the energy utilization efficiency is improved.

The invention discloses an intelligent power distribution energy-saving power supply system and method, relates to the technical field of power systems, and solves the technical problems that the user experience is poor due to the lack of consideration of the user participation degree in the prior art, and the efficiency of intelligent power distribution energy-saving power supply is low due to the fact that the electric quantity generated by renewable energy cannot meet the user demand. Obtaining predicted electricity consumption of the plurality of areas through an electric power prediction model; calculating a regional power utilization priority coefficient according to the regional data; the power station state is adjusted according to the predicted electricity consumption and the regional electricity consumption priority coefficient, the user participation degree is considered, whether the generating capacity meets the electricity consumption requirement or not is predicted, regulation and control are conducted in advance, the situation of insufficient power supply is reduced, and the user experience and the power supply efficiency are improved.

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 a laboratory air-conditioning temperature control system and method based on artificial intelligence, and relates to the field of laboratory temperature control. Optimizing the positions of the air supply and exhaust outlets by using a pre-constructed airflow analysis model; according to the optimized positions of the air supply and exhaust outlets, the air conditioning system is used for controlling airflow to flow to the corresponding air supply and exhaust outlets, and directional airflow from the first area to the second area is generated; according to laboratory electrical load data, an optimal charging and discharging strategy of an energy storage device is generated by using a reinforcement learning algorithm, and the reinforcement learning algorithm enables an intelligent agent to learn optimal strategies in different states by constructing a state space including electricity price, electrical load, energy storage electric quantity and photovoltaic power generation and setting a reward function with maximum load stability. Aiming at the low laboratory temperature control precision in the prior art, the temperature control precision is improved by simulating an airflow velocity field and radiation quantity distribution and optimizing the layout of air supply and exhaust outlets by using CFD simulation and a genetic algorithm.

The invention relates to the field of wind turbine generator control, in particular to a wind turbine generator operation control optimization method and system based on deep learning, and the method comprises the steps: obtaining the vibration, bearing temperature and generator winding temperature of a wind turbine generator, and the wind speed, wind direction and air density of the wind turbine generator; inputting all acquired data into a control parameter acquisition model, and outputting an ideal control parameter set of the current wind turbine generator; calculating an accurate rated wind speed; adjusting the control frequency based on the wind speed and the wind direction; each control parameter is adjusted; the wind turbine generator operation control optimization system comprises a data monitoring module, a control parameter acquisition module, a control frequency adjustment module and a pitch angle adjustment module. The method has the advantages that limitation of a traditional single-machine control strategy is broken through, especially when the wind speed is lower than the rated wind speed, the ideal increment of the pitch angle is actively adjusted, and therefore the total generating capacity of the wind power plant is improved.

The invention belongs to the technical field of smart power grids, and discloses a multi-energy collaborative optimization regional power peak regulation cloud service system and method. The method comprises the following steps: collecting regional electricity consumption time sequence data, and collecting photovoltaic power station key data and wind power station key data in unit time; preprocessing the key data of the photovoltaic power station and the key data of the wind power station to obtain preprocessed key data of the photovoltaic power station and preprocessed key data of the wind power station; inputting the preprocessed key data of the photovoltaic power station into a photovoltaic power generation prediction model, inputting the preprocessed key data of the wind power station into a wind power generation prediction model, and respectively obtaining predicted photovoltaic power generation and predicted wind power generation; inputting the regional electricity consumption time sequence data into the regional electricity consumption prediction model to obtain future unit time regional electricity consumption; according to the invention, the prediction accuracy of the regional predicted electricity consumption and the predicted generating capacity of the distributed energy power station is improved.

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 belongs to the technical field of power supply, and discloses a source network load storage power supply-based energy control method and system and a storage medium, and the method comprises the steps: determining a first photovoltaic power generation amount and a first power consumption demand amount of a prediction day at a set moment based on first historical data and prediction data, calculating a difference value between a first available power consumption amount and the first power consumption demand amount, and when the difference value is greater than a first preset value, establishing a cost objective function based on the capacity of the energy storage device, the difference value, the operation loss and maintenance loss of the energy storage device, and the electricity price cost of each time period in the prediction day, and charging the energy storage device based on the charging time and the charging electric quantity obtained by solving the cost objective function. And periodically determining a second photovoltaic power generation amount and a second power consumption demand amount in each set time period, and carrying out charging and discharging control on the energy storage device according to a source-grid-load-storage cooperative power supply mechanism. According to the method, the source network load storage power supply system can be comprehensively optimized through accurate prediction and control.

The invention provides an intelligent photovoltaic power distribution method, system and device and a storage medium, and relates to the technical field of electric energy distribution, and the method comprises the steps: predicting the target power generation amount of a photovoltaic power generation device in a preset time period through combining the environment information of the photovoltaic power generation device and the initial power generation amount at the current moment; obtaining a historical power distribution strategy of a plurality of electric devices corresponding to the photovoltaic power generation equipment, and generating an initial power distribution strategy of each electric device within a preset time length in combination with the target power generation amount and the historical power distribution strategy; and adjusting the initial power distribution strategy by combining the load state and the equipment characteristic of each electric equipment at the current moment, generating a target power distribution strategy, and performing power distribution on the electric equipment according to the target power distribution strategy within a preset duration. The method has the technical effects that the flexibility and pertinence of power distribution are improved, and the dynamic power utilization requirement of the electric equipment is met.

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.