1476 results about "Light storage" patented technology

The invention relates to the technical field of data processing, in particular to a building light storage and charging integrated energy management system, which is used for solving the problems of data processing delay and resource coordination mismatch caused by insufficient multi-source heterogeneous data fusion in photovoltaic power generation, energy storage and grid connection links. The system comprises a data acquisition module, a protocol conversion module, a data fusion module, a scheduling optimization module, an instruction generation module and a feedback module. The protocol conversion module carries out Modbus, CAN bus and IEC 61850 protocol analysis on the photovoltaic output time sequence data, the energy storage charging and discharging state data and the power grid dispatching instruction to generate standardized data streams. And the safety verification module intercepts the overrun instruction and triggers an amplitude limiting strategy. Through protocol unification, dynamic fusion and closed-loop control, the system realizes improvement of photovoltaic consumption rate, reduction of energy storage circulation loss and optimization of power grid frequency modulation deviation, and significantly improves the efficiency and economy of light storage and charging cooperative operation.

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

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 invention relates to the technical field of energy storage control, in particular to a grid-connected type light storage and charging energy storage control system which comprises a power bearing capacity monitoring module, a light storage power adjusting module, a grid-connected power feedback module, a power grid stability margin calculation module and an energy storage power dynamic control module. According to the method, the voltage and current data of the grid-connected point are acquired in real time, the power transmission capacity is calculated in combination with the rated voltage range and the safety power upper limit, the power adaptation precision of the grid-connected point is improved, the photovoltaic power generation grid-connected proportion is dynamically optimized on the basis of comparison between load growth rate calculation and the power adjustment range, and photovoltaic power output curve change rate analysis is combined. Power output fine control, calculation of the load change rate and combination of power grid power factor data are achieved, a charging and discharging mode adjustment strategy is optimized, the frequency change rate and fluctuation degree are monitored, the stability margin of the power grid is enhanced, frequency deviation of the power grid caused by load fluctuation is avoided, and the new energy consumption rate and the operation reliability of the power grid are improved.

The invention discloses an optical storage direct-flexible system power scheduling method based on market model prediction response, which comprises the following steps: determining the composition of an optical storage direct-flexible system, and collecting operation state data and external environment data; constructing a hybrid prediction model, and predicting the market price and the uncertainty interval of the market price in the scheduling period; establishing a power scheduling optimization model, and solving to obtain an optimal power scheduling strategy; according to the optimal power scheduling strategy, a control instruction is issued to each control unit of the optical storage direct flexible system, and cooperative adjustment is carried out through a bidirectional coupling feedback mechanism of voltage and power; the market price and the system operation state are monitored in real time, and when the market price deviation exceeds a price deviation threshold value, scheduling strategy online correction is triggered. According to the robust optimization architecture, traditional power scheduling is converted into a confrontation game structure between the system and the market, and the resistance of the system to extreme price situations is improved while economic benefits are guaranteed.

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention belongs to the field of microgrid resource capacity optimization. The invention provides a power distribution network wind and light storage capacity optimization method considering multi-microgrid energy storage cooperation. The method comprises the following steps: step 1, establishing a wind and light combined operation power information data set considering spatial correlation during multi-microgrid source load fluctuation; and step 2, multi-microgrid wind and light storage capacity configuration optimization is realized by using a reinforcement learning algorithm. And step 3, complementing the wind-light fluctuation scene with few samples by using a transfer learning algorithm. Based on deep fusion space-time correlation modeling, multi-agent reinforcement learning and cross-domain transfer learning, a multi-microgrid energy storage collaborative optimization framework with dynamic adaptive capacity is provided. According to the method, the deep association rule of the multi-dimensional operation data of the micro-grid group can be analyzed, global optimal capacity configuration is realized through a coevolution mechanism of an intelligent algorithm, and a brand new solution is provided for solving the problem of power distribution network optimization under high-proportion new energy access.

The invention relates to the field of short-term load prediction and power dispatching control of a power system, in particular to an energy output optimization dispatching method and system considering a random source load virtual power plant, and designs a virtual power plant double-layer optimization dispatching model. The lower-layer model constructs a user load prediction model of an improved Transform, and then a renewable energy output model is established by using a scene generation method; the upper layer model takes the power generation cost minimization of the wind-light storage and combustion virtual power plant as a target, considers constraint conditions such as gas turbine output power, ramp rate, energy storage charging and discharging power, electricity storage quantity and power balance, and proposes a single guide target function comparison algorithm considering random source load to carry out optimization solution on a virtual power plant optimization scheduling target function; and planning and deciding the output of each energy source in the next 24 hours. Experiments show that the new energy consumption level can be remarkably improved under the random source load condition, and better economic benefits are obtained while stable operation of a power distribution system is promoted.

The invention discloses a wind and light storage adjustment inertia and frequency coordination control method, and belongs to the technical field of new energy grid-connected power generation. The method is used for inertia support and frequency cooperative control of a new energy power station containing wind power generation, photovoltaic power generation, energy storage and a phase modifier, and comprises the steps of system minimum inertia demand evaluation, wind and light storage and adjustment inertia time constant evaluation, calculation of minimum inertia support time needed by the system and a wind and light storage and adjustment inertia distribution strategy. According to the wind and light storage and adjustment inertia and frequency coordination control method, the inertia supporting capacity of a new energy grid-connected system is effectively improved, wind, storage, photovoltaic and phase modifier inertia supporting tasks are effectively distributed, and the system frequency safety setting requirement is flexibly met. By calculating the minimum inertial supporting time, inertial supporting and exiting are carried out in time, power back-filling in the frequency recovery stage is avoided, and the frequency modulation burden of the system is relieved.

The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery, and belongs to the field of active power distribution network operation analysis. The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery. The method comprises the following steps: establishing an energy storage system refined operation model in a wind-light storage micro-grid; on the premise of maximum income and maximum new energy consumption, the method has the beneficial effects that the new energy power output fluctuation is responded in time, so that the charge and discharge control of the energy storage system is efficient and accurate, and the purposes of improving the energy storage utilization efficiency, the charge and discharge control accuracy and the power output stability are achieved; according to the improved multi-objective grey wolf optimization algorithm, an initial population strategy, a population individual iteration updating strategy and a multi-objective optimal solution solving mechanism are improved, and the convergence speed, the solution set diversity and the calculation efficiency of the multi-objective grey wolf optimization algorithm are improved through multi-strategy cooperation; the problems of excessive uncertainty and single target of the existing wind and light storage micro-grid are effectively solved.

The invention relates to a light storage and charging cooperative control method, electronic equipment and a medium. The method comprises the following steps: collecting related meteorological data influencing photovoltaic power generation, calculating a Spearman correlation coefficient, determining key meteorological characteristics, and constructing a data set; and predicting the photovoltaic generation power based on the data set by using a feature attention mechanism and a GRU model. Meanwhile, historical data of the charging piles at different geographic positions are collected and processed, space and time characteristics are extracted by using a K-Means clustering algorithm, and a charging pile load short-term prediction model is constructed. And according to the prediction results of the photovoltaic power generation and the charging pile load, a multi-target optimization model is established by using an MOPSO algorithm, and energy collaboration and optimization control among photovoltaic power generation, energy storage and electric vehicle charging are realized.

The invention discloses a building integrated energy system optimization scheduling method giving consideration to the comfort level of a light storage soft environment, and relates to the field of building energy system optimization scheduling, and the method comprises the steps: building a photovoltaic output uncertainty model based on Beta distribution according to typical daily solar radiation intensity data; secondly, constructing an energy storage model; thirdly, a comfort level quantitative model considering temperature regulation and control of the indoor air conditioner and the electric water heater is constructed, and the influence on the environmental comfort level is reflected laterally; and finally, according to the various formed models, constructing a building integrated energy system optimization scheduling model considering the light storage soft comfort degree, and performing solving based on a mixed integer linear programming method. The environment comfort level of the building integrated energy system can be quantified, the flexibility of the building integrated energy system is improved by introducing photovoltaic and energy storage equipment, flexible scheduling of load resources is facilitated, peak load shifting is better achieved, the operation cost of the building integrated energy system is reduced, and the operation efficiency of the building integrated energy system is improved.

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

The invention provides a photovoltaic output prediction method and a distributed optical storage transformer area active / reactive support capability optimization method, and belongs to the technical field of power system automation. Comprising the following steps: constructing a prediction model based on meteorological information, extracting multi-scale features of meteorological data, reducing dimensions through kernel principal component analysis, predicting photovoltaic power through a long-short-term memory network, quantifying the uncertainty of the photovoltaic power, and forming a prediction interval. Based on a photovoltaic power prediction result, a robust nonlinear programming model is established, the sum of absolute values of active / reactive power transmitted to a main power grid by a light storage area is maximized as a target, and linear and nonlinear constraints such as energy storage operation, power balance, equipment capacity and power factors are considered at the same time. The robustness under the photovoltaic output uncertainty is ensured through a budget uncertainty set; the model is solved by adopting a sequential quadratic programming algorithm. According to the method, the photovoltaic power prediction precision is effectively improved, and organic combination of high-precision prediction and robust nonlinear optimization is realized.

The invention discloses an optical storage and charging cooperative off-network control method based on an energy router, and relates to the technical field of optical storage and charging cooperative control, and the method comprises the steps: collecting energy router grid-connected point off-network control data and environmental parameters in real time, carrying out the preprocessing of the collected data, and storing the processed data into a time sequence database; constructing a multi-level prediction model to predict load, power generation and power grid condition data, and dynamically updating system operation parameters according to a prediction result; according to the off-network control data, performing intelligent off-network pre-judgment to switch a system operation mode, and performing off-network operation optimization control; and continuously monitoring the state of the power grid, recovering grid-connected operation after the bus voltage is stable, and carrying out optical storage and charging collaborative off-grid control based on the energy router. According to the invention, long-time off-grid operation of the DC micro-grid is realized, and the power supply reliability and the equipment safety of off-grid operation are improved.

The invention relates to the technical field of electric power operation and maintenance, in particular to a distributed optical storage unit cooperative scheduling control method and system, the method is applied to a distributed optical storage system comprising a plurality of optical storage units, each optical storage unit comprises a photovoltaic unit, an energy storage unit and a load unit, and is configured with a local unit controller. Comprising the following steps: collecting real-time operation data of an optical storage unit, and extracting local collaborative scheduling characteristics and a local DC bus voltage instantaneous fluctuation ratio from the real-time operation data; each unit controller inputs the corresponding local cooperative scheduling feature into a preset local scheduling strategy generation model, and outputs an initial scheduling action instruction set; and performing global voltage stability evaluation on the distributed optical storage system based on the local direct current bus voltage instantaneous fluctuation rates corresponding to all the optical storage units, and calculating to obtain a system voltage fluctuation index. The distributed local control capability and the global collaborative demand are organically fused, and the global voltage stability and the collaborative scheduling consistency are ensured.

The invention discloses a wind and light storage coordination control method and system for a virtual synchronous machine, and the method comprises the steps: building a wind power plant-energy storage power station double-layer response mechanism at a first stage, and employing a multi-time scale coordination control strategy for a second-stage energy storage system; and in combination with the virtual synchronous control characteristics of the optical storage power station, establishing a multi-scene optimization model of a wind and light storage multi-source collaborative double-layer coupling mechanism, and realizing dynamic optimization of output of each unit among inertia support, frequency modulation capacity and energy storage health state. According to the method, wind power plant rotor kinetic energy release combined overspeed load shedding control, a fuzzy control means based on an energy storage charge state and a VSG control means based on a virtual inertia and damping coefficient self-adaption photovoltaic system are considered, then wind and light energy storage frequency modulation coordination is optimized, and the system inertia safety is effectively improved on various working conditions and different time scales.

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, which relates to the technical field of optical storage and charging, discloses an optical storage and charging cooperative control method for a transformer area, and the method comprises the steps: carrying out the intelligent and multivariate collection and prediction through intelligent equipment, constructing a function through collected data, solving an optimal solution, selecting an optimal optical storage and charging cooperative control strategy from the optimal solution, generating a corresponding control instruction, and carrying out the transmission of the corresponding control instruction. Cooperative regulation and control of a photovoltaic power generation system, an energy storage system and a charging pile system are achieved, a feedback and correction mechanism is further established, and it is ensured that the photovoltaic storage and charging system stably operates according to a preset strategy. According to the transformer area light storage and charging cooperative control method, a light storage and charging cooperative control mechanism is established by comprehensively considering photovoltaic power generation, an energy storage system and a charging pile system, power resource distribution in a transformer area is optimized, maximum consumption of photovoltaic power generation is achieved, the light abandoning phenomenon is reduced, meanwhile, unnecessary charging and discharging loss of the energy storage system is reduced, and the power utilization rate of the transformer area is improved. And the impact of disordered charging of the charging piles on a power grid system is relieved, so that the overall operation performance of a transformer area power system is improved.

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.

The invention discloses a wind and light storage and charging micro-grid distribution optimization method and system. The method comprises the following steps: acquiring parameter data of each node in a micro-grid in real time; constructing a load demand prediction model, and automatically adjusting the scheduling weight coefficient of the wind and light storage and charging equipment according to the weather type; based on a model output result, establishing a matching relationship between the line impedance and the load emergency degree, generating a power supply path set, and creating a power path selection mechanism based on a dynamic planning algorithm; calculating real-time charge-discharge efficiency based on the equivalent resistance parameter of the power supply path, and dynamically adjusting a charge-discharge rate threshold according to a prediction error; acquiring a real-time monitoring load curve, calculating the similarity between the load curve and historical data, and matching and identifying a potential sudden change risk; and according to a distributed negotiation protocol, each node achieves a scheduling consensus through a priority broadcasting mechanism. The method has the advantages that through load prediction and dynamic scheduling, optimal scheduling, power fluctuation stabilization and sudden change risk identification of the wind and light storage and charging equipment are realized.

The invention relates to the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust optimization configuration method, device and equipment and a medium. Establishing a power grid side wind and light energy storage optimization configuration model considering inertia constraint; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving; converting the two-stage distribution robust optimization configuration model into a mixed integer linear programming model by using a linearization technology; and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device. According to the method, the economical efficiency and load fluctuation can be balanced, and the problems of frequency instability and the like of a large-scale wind and light energy grid-connected power grid are avoided by considering inertia constraint.

The invention belongs to the technical field of new energy charging, and particularly relates to a light storage and charging integrated new energy charging station which comprises a photovoltaic power generation module, an energy storage module, a charging module and an intelligent control unit. The photovoltaic power generation module predicts power generation power through an LSTM neural network in combination with meteorological data, and dynamically adjusts a prediction time window based on a weather type; the energy storage module is directly connected with the photovoltaic direct current side to improve energy efficiency; the charging module obtains the SOC / SOH state of the vehicle and the user demand through the data acquisition unit, and generates a priority coefficient by using a scoring model. The system dynamically distributes photovoltaic power, energy storage power and power grid power through a demand grading module, and when it is detected that the photovoltaic output deviation exceeds a threshold value or the energy storage SOC is insufficient, a power redistribution and compensation mechanism is automatically triggered. According to the invention, efficient utilization of renewable energy sources, intelligent scheduling of an energy storage system and differentiated supply of charging services are realized, and the problems of low photovoltaic consumption rate and response lag of a traditional charging station are solved.

The invention provides a multi-energy coupling complementary light storage and charging management system. The multi-energy coupling complementation light storage and charging management system comprises a photovoltaic power generation module, an energy storage module, a lithium battery pack, a power grid input end, an energy conversion module, an energy storage management module, an energy scheduling module, an energy storage management module and a power supply management module, electric energy input by a photovoltaic power generation module, an energy storage module and a power grid is allocated through a multi-energy coupling algorithm, a charging management module is used for controlling the stability of the electric energy output outwards, and system control software is used. According to the multi-energy coupling complementary light storage and charge management system, through an innovative multi-energy complementary technology and an energy efficiency optimization algorithm, the problem of energy loss generated in the energy switching process in the prior art is effectively solved, a deep reinforcement learning algorithm and a dynamic energy distribution model are adopted, and the energy efficiency is improved. And electric energy from different sources is intelligently dispatched.

The invention discloses an island micro-grid reliability evaluation method and device considering a hybrid energy storage system, and the method comprises the steps: obtaining the operation parameters of an island micro-grid energy storage system, considering the uncertainty of load output, and the charge and discharge and capacity constraints of the energy storage system, building a load output model and an energy storage charge and discharge model, state parameters of the hybrid energy storage system are obtained, a multi-state model of the hybrid energy storage system is established, the state probability is solved, and a reliability index of the hybrid energy storage system is obtained; establishing a minimum load reduction strategy model on the basis of considering the load importance; and according to the hybrid energy storage system multi-state model, the energy storage charging and discharging model, the load output model and the minimum load reduction strategy model, constructing an island photovoltaic micro-grid reliability evaluation program containing the hybrid energy storage system, and outputting a reliability index of the hybrid energy storage system. And reliability evaluation can be carried out on the island photovoltaic micro-grid system containing the wind and light storage.

In order to solve the defects in the prior art, the invention provides a park energy storage capacity optimal configuration method and system and a storage medium, and the method comprises the steps: 1, collecting and obtaining the wind and light historical data and load historical data of a to-be-analyzed park; 2, preprocessing the historical data, analyzing factors influencing the wind, light and load output of the park, screening out a feature input set, and normalizing the processed historical data; 3, constructing a wind-light and load output prediction model, taking the feature input set and historical data as the input of the prediction model, and performing short-term power prediction on the wind-light and load; 4, constructing an energy storage capacity optimization configuration model of the wind and light storage system based on a short-term power prediction result; and 5, solving the optimal configuration model of the energy storage capacity of the wind and light storage system to obtain an optimal configuration result of the energy storage capacity of the wind and light storage system. The method effectively improves the wind and light absorption of the park, reduces the wind and light abandoning rate, and guarantees the economic benefits of the park.

The invention discloses a photovoltaic power generation intelligent control system of photovoltaic panels in an optical storage direct flexible system, and relates to the technical field of photovoltaic power generation, the intelligent control system comprises a plurality of photovoltaic panel assemblies, the plurality of photovoltaic panel assemblies comprise main node assemblies, one main node assembly is arranged every 5-10 groups of photovoltaic panel assemblies, and the main node assemblies are connected with the photovoltaic panel assemblies; the master node assembly is configured with a high-precision double-axis sun sensor and an edge calculation module and is responsible for collecting a solar azimuth angle, a current inclination angle of the assembly and environment temperature and humidity data in real time, and the slave node assembly is configured with a single-axis inclination angle sensor and adjusts a tracking angle by receiving a master node instruction. Specifically, a master-slave node heterogeneous design is adopted, the slave node only needs a single-axis tilt angle sensor and a wireless communication module, the hardware configuration number is reduced, and cost control is achieved. According to the method for photovoltaic panel tracking optimization, the control instruction quantity of the system is reduced, the central control pressure is remarkably reduced, and the problems of cost and complexity during large-scale application of photovoltaic panel tracking are solved.

The invention discloses an analysis method, system, medium and equipment adaptive to faults of a water-optical storage network source system, and relates to the technical field of power system fault analysis. The system comprises a multi-source data acquisition and preprocessing module, a fault feature extraction and classification module, a fault positioning module, a fault influence evaluation module and a dynamic updating and self-adaption module; the method comprises the following steps of multi-source data acquisition and preprocessing, fault feature extraction and classification, fault positioning and influence evaluation, and dynamic updating and adaptive adjustment. The invention provides a fault analysis method adaptive to a water-light storage network source power system, various faults in the complex power system can be comprehensively and accurately analyzed, and rapid and accurate fault type identification, fault position positioning and fault influence range evaluation are realized by comprehensively considering the characteristics of each component of the system. Powerful support is provided for timely and effective fault processing, and stable and reliable operation of a power system is guaranteed.

The invention discloses an agricultural and pastoral area water network system carbon reduction and efficiency improvement optimization operation method and system. The method comprises the following steps: collecting agricultural and pastoral area data, constructing an agricultural and pastoral area database, constructing a dynamic coupling model, and optimizing model parameters; constructing a crop planting structure optimization model, solving the model to obtain a quarterly planting planning scheme, and inputting the dynamic coupling model to obtain a corresponding collaborative scheduling scheme; constructing a daily scale scheduling model, solving the model to obtain a daily scheduling scheme set, and screening an optimal daily scheduling scheme; formulating a water and electricity storage strategy, and adjusting operation of a water pump and wind and light storage equipment; and constructing an agricultural and pastoral area water network system real-time monitoring platform based on a digital twinborn technology, generating a control strategy and a carbon reduction and efficiency improvement evaluation report, and optimizing and adjusting parameters in the dynamic coupling model. Cooperative scheduling of water resources, power resources and agricultural loads is realized, the utilization rate of renewable energy sources such as wind and light is improved, and the overall operation benefit of the system is improved.