917 results about "Optical storage" patented technology

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 provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

The invention relates to the technical field of data processing, and discloses a cold and hot data exchange method and system based on optical storage and a storage medium. The method comprises the following steps: performing access feature acquisition and analysis on data in the optomagnetic hybrid storage system, calculating an access life cycle factor, generating a logic interval heat evaluation result, performing temperature grading, formulating a cold and hot data placement strategy, generating a data placement mapping table, executing batch migration according to priority, and updating a data position record. According to the method, the problem of how to realize logic interval hot degree accurate identification, temperature grade dynamic grading, storage mapping optimal matching and batch migration task efficient organization under the optomagnetic hybrid architecture is solved.

The invention relates to the field of optical storage, and particularly discloses an optical storage system cooperative control method and device, a terminal and a medium, and the method comprises the steps: collecting multi-dimensional data in real time, including photovoltaic array data, user side load data, power grid side information data, meteorological data and weather forecast information; generating a photovoltaic power generation power prediction curve, a user load demand prediction curve and a power grid electricity price prediction curve by using the multi-dimensional data through a machine learning model; and taking the current state of the optical storage system and each prediction curve as input parameters, carrying out optimization problem solving based on a multi-objective optimization function and constraint conditions through a model prediction control algorithm, generating an optimal control sequence in a period of time in the future, and controlling corresponding equipment through the optimal control sequence. According to the method, the response speed to uncertainties such as illumination abrupt change and load fluctuation is increased, source-storage-load-network coordination is realized, the sub-optimal problem of independent control of each unit is avoided, the operation cost is reduced, and renewable energy consumption is improved.

The invention provides an optical storage parallel network control method, which comprises the following steps: calculating the current load rate percentage of each residual unit according to the connection relation and electrical path information of the residual units after isolation; evaluating the available margin capacity and the frequency regulation bearing capacity of the remaining units, matching the available margin capacity and the frequency regulation bearing capacity with a specific power value and a frequency regulation requirement which need to be compensated, and determining a feasible unit combination scheme for fault recovery; generating a power redistribution list and a frequency adjustment responsibility distribution table according to the direct current bus connection mode of the optimal unit recombination, and obtaining an output power set value and a frequency adjustment parameter of each unit after adjustment; and extracting the continuous power supply time value of the optical storage parallel network, updating the total power supply capacity value of the optical storage parallel network, and maintaining the continuous power output and frequency stability after the fault of the optical storage parallel network.

The invention relates to a reliability evaluation method and system for a flexible AC / DC power distribution network containing an optical storage system in the field of flexible AC / DC power distribution networks. The method comprises the following steps: collecting photovoltaic data, energy storage data, AC / DC bus data and the like in real time; constructing a multi-time scale dynamic topology model, and establishing a failure probability distribution function; load flow calculation based on an improved forward-backward substitution method; adopting an N-1 + N-1-1 composite fault screening strategy to simulate a typical disturbance scene; establishing a vulnerability evaluation matrix to calculate power failure probability and other indexes; designing an optimal scheduling strategy based on model predictive control; multi-time-scale reliability evaluation is carried out; and outputting an evaluation report and a topology reconstruction suggestion. The system comprises a data acquisition unit, a dynamic modeling unit and the like, and an evaluation and analysis unit is internally provided with an algorithm module and is configured with an FPGA acceleration card. The method breaks through the limitation of traditional steady state evaluation in the technology level, improves the load flow calculation speed in the calculation efficiency level, reduces the operation cost in the application value level, improves the energy storage efficiency, and provides support for reliability evaluation of a novel power system.

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 provides an optical storage system intelligent scheduling and fault prediction method and device, equipment and a medium, and belongs to the technical field of intelligent optical storage, transportation and maintenance, and the method comprises the steps: collecting the data of an optical storage system in real time, and carrying out the preprocessing; inputting the preprocessed data into a multi-modal fusion model, and performing unified feature representation through a cross attention mechanism to obtain fusion features; inputting the fusion features into a prediction model for fault prediction and residual service life prediction; generating a state space formed by a fault type, a fault probability and a real-time electricity price according to a prediction result through a reinforcement learning algorithm, and setting a scheduling strategy which takes the charging and discharging power of the energy storage battery pack as an action space, and setting a reward function; and constructing an experience playback pool according to the actual operation data of the optical storage system, and regularly updating the strategy network of the reinforcement learning algorithm. According to the invention, the data is collected through the multi-mode sensor, the deep learning and reinforcement learning are combined, the intelligent fault prediction and dynamic scheduling of the optical storage system are realized, and the operation efficiency and reliability are improved.

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 provides a distributed robust optimization scheduling method for an optical storage charging and discharging integrated power station, and aims to solve the influence of multiple uncertain factors in an optical storage charging and discharging system on a power station scheduling decision and improve the robustness and real-time response capability of the system. According to the method, a distributed robust optimization framework is constructed, each node autonomously decides on the basis of boundary variables exchanged by local information and neighbor nodes, operation state changes are quickly responded through information interaction and cooperation, global cooperative scheduling is realized based on a distributed solving technology of a consistency algorithm, the calculation complexity is reduced, and the communication overhead is reduced. A multi-time scale model is introduced, the uncertainty of day-ahead scheduling is predicted based on a scene tree method, and a rolling optimization strategy is adopted for real-time scheduling. A charging pile intelligent scheduling strategy is formulated, bidirectional interaction with a power grid is realized, and the power grid stability and the resource utilization rate are improved. According to the invention, efficient, stable and economical operation of the optical storage charging and discharging integrated power station is realized, and the real-time scheduling requirement of a large-scale optical storage charging and discharging station is met.

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 an optical storage charging station robust optimization method considering the travel behavior deviation of a charging user, and the method comprises the steps: depicting the path selection and charging behaviors of an electric vehicle user under different perception deviations through collecting the multi-dimensional data of a power network and a traffic network in real time, and employing a random user balanced mixed traffic flow distribution model; coupling the traffic and the power network through a traffic-power coupling network time-space correlation constraint; and establishing a two-stage robust optimization model of the optical storage charging station by integrating the photovoltaic output of the charging station and the behavior perception deviation multi-dimensional uncertainty of the travel user of the traffic network. An efficient solving algorithm is designed for a specific model, an optimal operation strategy of the power distribution network and photovoltaic charging station equipment in a worst scene is finally generated, dynamic optimal balance of operation economy and robustness of the power distribution network is achieved, the operation cost is reduced while the safety margin of the system is guaranteed, and the system reliability is improved. And the cooperative regulation and control capability of the complex coupling system is obviously improved.

The invention provides a coordinated control method and device for flexible interaction of an optical storage and charging micro-grid and a power distribution network, and belongs to the technical field of intelligent power grids and distributed energy management. According to the method, through a day-ahead, intra-day and real-time three-layer coordination control architecture, the system operation cost is optimized, and the power grid stability is improved. The day-ahead layer generates a 24-hour scheduling plan based on photovoltaic output prediction, electric vehicle charging requirements and time-of-use electricity price; the intra-day layer performs rolling optimization in combination with real-time photovoltaic / load fluctuation, and dynamically allocates V2G and energy storage adjustment tasks; the real-time layer adopts adaptive droop control, monitors power grid abnormity and triggers cross-layer cooperation, and also sets photovoltaic fluctuation compensation and island mode switching conditions. Collaborative optimization is realized among the layers through bidirectional data channels. According to the method, the defect of interactive control of the optical storage and charging micro-grid and the power distribution network in the prior art is overcome, the adaptability of the system to distributed energy fluctuation is enhanced, the V2G resource potential is fully played, and the coping capacity under the abnormal condition of the power grid is improved.

The invention relates to the technical field of optical storage direct-flexible scheduling, and discloses a method for establishing an energy storage scheduling model of a multi-node optical storage direct-flexible system. The method comprises the following steps: acquiring real-time operation data of each node of a system, including a photovoltaic output fluctuation curve, an energy storage charge state, a load demand time sequence, a power grid interaction power limit value and an environmental irradiance change characteristic, and performing multi-dimensional alignment processing on the real-time operation data to form a multi-dimensional operation data set; performing operation state mapping topology construction processing on the data set, and separating out target operation data and state indication data; based on the target operation data, in combination with scheduling priority configuration information of each node, generating a multi-node collaborative energy storage scheduling initial model containing an energy storage charging and discharging time sequence strategy and a power distribution scheme; and identifying node operation abnormal characteristics based on the state indication data, dynamically reconstructing the initial model according to the node operation abnormal characteristics, and outputting the updated energy storage scheduling model to guarantee stable and efficient operation of the system.

The invention discloses a 5G base station optical storage integrated power supply control system, and relates to the field of 5G base station optical storage integrated power supply control, and the system comprises a detection module which detects the output voltage of a photovoltaic array, the SOC of an energy storage battery and the impedance parameter of a DC bus, and obtains a detection result; and the 5G wireless sensing module monitors reference signal receiving power and channel state information by initializing a 5GNR physical layer chip based on a detection result, transmits the reference signal receiving power to the micro-control unit through an SPI interface, and reads the maximum power point voltage and current of the photovoltaic module to obtain a time sequence and a photovoltaic array. According to the method, the Lyapunov exponent is solved through phase-space reconstruction and the Wolf algorithm, the chaos state flag bit and the load impedance are generated, the parameters of the LLC resonant converter are dynamically matched, the nonlinear dynamics theory is applied to the field of power supply control, the working modes can be adaptively switched through intelligent recognition and utilization of chaos features, and the power supply efficiency is improved. And optimal scheduling of energy is realized while the ZVS state is ensured.

The invention discloses a three-port soft switching optical storage servo all-in-one machine based on device multiplexing and a modulation method. The all-in-one machine comprises a DC-AC servo driver, a low-voltage DC energy storage module, an auxiliary unit, a bus capacitor and a photovoltaic module. The DC-AC servo driver comprises three bridge arms, and each bridge arm is provided with a bridge arm upper tube and a bridge arm lower tube; the low-voltage direct-current energy storage module comprises an energy storage battery, an energy storage switch tube S7 and a direct-current energy storage inductor; the photovoltaic module comprises a photovoltaic power generation panel, a photovoltaic inductor, a photovoltaic switch tube S8 and a diode D1; the auxiliary unit comprises an auxiliary switch tube Sa, an auxiliary resonant inductor Lr and an auxiliary capacitor Cr. According to the invention, an integrated resonant soft switching architecture is innovatively adopted, zero-voltage / zero-current switching of all power switching devices is realized, and the problem of loss caused by a traditional hard switch is thoroughly solved; and by optimizing a device multiplexing technology, the number of switching tubes is greatly reduced while the soft switching performance is ensured, so that the system efficiency is improved in a breakthrough manner.

The invention discloses a multi-source data fusion-based optical storage and charging micro-grid optimal configuration method. The method comprises the steps of S1, deploying a photovoltaic monitoring module, an energy storage BMS and a charging pile controller; s2, inputting historical irradiance, temperature and charging record data based on an LSTM algorithm; s3, establishing a multi-target optimization model, and taking the lowest system operation cost and the highest photovoltaic consumption rate as targets; s4, dynamically executing the optimization strategy; the invention relates to the technical field of new energy micro-grids. According to the multi-source data fusion optical storage and charging micro-grid optimal configuration method, the problem of power supply guarantee under different power demands is solved. According to actual requirements, an electric energy flowing path in an electrical architecture is designed, a path with the highest transmission efficiency is planned, disordered power utilization of a charging pile is met through the quick response capability of energy storage, and then an energy management strategy under a light storage and charging multi-energy complementary platform is determined. And finally, under the condition that the charging requirement is met, the photovoltaic and energy storage effects are fully exerted, and the economic maximization of the whole set of system is realized.

The invention relates to the technical field of battery modeling and photovoltaic energy storage systems, and discloses a self-adaptive battery modeling method for direct current bus voltage support of an optical storage system. The method comprises the following steps: establishing a basic model comprising a direct-current voltage source, an ohmic internal resistor and an RC polarization network; the voltage drop depth is obtained by monitoring the voltage of the direct-current bus in real time and used for correcting the polarization resistance; and performing differential operation on the voltage to obtain a change rate so as to adjust the polarization capacitance. And dynamically optimizing the battery model by combining correction results of the two, and realizing adaptive updating of model parameters. And applying the optimization model to the optical storage integrated grid-connected system. The adaptability of the battery model to the dynamic working condition is improved.

An insulation impedance measurement system and method, and an optical storage hybrid energy system. The insulation impedance measurement system is used for measuring the insulation impedance in an optical storage hybrid energy system. In the insulation impedance measurement system, one end of a first auxiliary measurement circuit is connected to a negative electrode of each photovoltaic string and a negative electrode of a battery module, one end of a second auxiliary measurement circuit is connected to a positive electrode of the battery module, and the other end of the first auxiliary measurement circuit and the other end of the second auxiliary measurement circuit are connected to a reference voltage end; a processor controls an inverter module (110) to set the output voltage of each photovoltaic string and the bus voltage of a line where the battery module is located, controls, on the basis of the set voltages, the first auxiliary measurement circuit and the second auxiliary measurement circuit to measure the voltages of the negative electrode of each photovoltaic string and of the negative electrode of the battery module for the reference voltage end, and calculates the impedance value of each insulation impedance.

The invention relates to the technical field of power systems, and discloses a smart base station optical storage capacity optimal configuration method, which comprises the following steps: acquiring historical load and photovoltaic output data of a smart base station, and constructing a relationship between temperature and load power of high-power equipment; taking the minimum net expenditure as an objective function, taking the electricity purchase cost, the energy storage investment cost, the energy storage loss cost and the income of participating in the peak regulation auxiliary service of the power grid into the objective function, and constructing an operation optimization model of the optical storage grid-connected system; solving the operation optimization model of the optical storage grid-connected system, and determining an initial optical storage capacity configuration scheme; and dynamically correcting the initial optical storage capacity configuration scheme based on communication look-ahead prediction and the thermal inertia buffer effect of the building, and determining the optimal optical storage capacity configuration scheme of the smart base station. Accurate configuration and efficient operation of the optical storage system are realized from multiple dimensions of communication, energy and thermal environment, and systematic technical support is provided for green, low-carbon and intelligent development of a smart base station.

An inertia cooperative control method of an integrated optical storage alternating current and direct current hybrid micro-grid comprises the steps that firstly, according to static and dynamic power capacities of energy storage units in direct current sub-grids and allowed direct current voltage deviation, sub-grid equivalent virtual resistance and virtual capacitance are calculated; secondly, RC + R type parallel virtual impedance parameters are distributed according to the power proportion of each energy storage unit, and multi-energy-storage cooperative response power disturbance according to the capacity is achieved; then, a phase angle is generated based on the mapping relation between direct-current side voltage and frequency, an alternating-current voltage amplitude instruction is determined in combination with reactive power droop control and feeder impedance voltage drop compensation, and a dq-axis voltage instruction is generated through virtual impedance compensation; normalized three-phase modulation waves are obtained through voltage-current double closed-loop control and dq / abc coordinate transformation, and six paths of PWM signals are generated to drive an interconnection converter after the normalized three-phase modulation waves are compared with triangular carrier waves. The method does not need to depend on external communication, has the capabilities of autonomous inertia support, voltage and frequency coordinated regulation and reasonable multi-source power distribution, and improves the stability and dynamic response performance of the system under island operation.

The invention discloses a distributed hierarchical cooperative control method and system for an optical storage all-in-one machine cluster, and the method comprises the steps: collecting voltage, frequency and harmonic data, and calculating a global voltage deviation, a frequency deviation and a harmonic content consensus value; calculating a unified transient support index judgment working condition based on the consensus value, and generating a global virtual inertia and impedance demand in emergency; calculating a schedulable potential index of each node based on local data of the node; distributing the requirements for the first time according to the indexes, and adjusting the parameters of the inverter to execute control after the nodes which do not meet the requirements are distributed for the second time; a three-layer architecture of'decision-aggregation-execution 'is constructed, a global state consensus and a nearby-global secondary distribution strategy are realized in combination with a consistency algorithm, a set of'local priority-adjacent support-global bottom-taking' allocation mechanism is formed, the adaptive capacity of the optical storage cluster to complex working conditions and the regional power quality are improved, and the optimal power quality is improved. And the grid-connected stability and the absorption level of the large-scale distributed energy in the weak power grid environment are remarkably improved.

The invention discloses a control method and device for multi-level cooperation of an optical storage and charging comprehensive supply station, and relates to the technical field of optical storage and charging station control, and the method comprises the steps: obtaining optical storage and charging information, constructing a three-level control structure of an equipment level, a station level and a distribution network level according to the optical storage and charging information, and carrying out the control of the equipment level, the station level and the distribution network level based on the equipment level control architecture. And obtaining a charging strategy according to the charging vehicle data information and the charging pile data information. According to the invention, through constructing an equipment-level, station-level and distribution-network-level three-layer cooperative control architecture, fine management of photovoltaic power generation, an energy storage system, a charging pile and V2G equipment is realized, a constant-current, constant-voltage and trickle-current staged control charging strategy is introduced, and charging parameters are accurately adjusted in combination with a battery health degree, an SOC target value and an environment temperature correction coefficient, so that the charging efficiency is improved. And a group charging and group control mechanism based on the priority and the weight coefficient is designed, the charging power of all vehicles in the comprehensive supply station is adjusted, and the resource utilization rate is improved while the charging speed and the service life of the battery are considered.

The invention discloses a power control method, system, equipment and medium for an optical storage and charging station, and belongs to the field of optical energy storage. A health degree factor is determined based on collected equipment operation data; determining a predictive factor based on the collected photovoltaic data; based on the power allocation weight and operation factors, a weight function is established to calculate the instruction weight of each device, and the operation factors comprise a planned maintenance factor, a sudden failure factor, an energy storage correlation factor, a health degree factor and a prediction factor of the parallel devices; calculating to obtain a power instruction of each parallel device; and controlling the parallel equipment to operate according to the power instruction through the station controller. According to the method, the bearable degree of the equipment is measured by calculating the dynamic weight comprehensively obtained on the basis of the health factor, the prediction factor, the maintenance factor, the sudden failure factor and the energy storage correlation factor, and the power instructions with inconsistent stagger degrees are distributed according to the actual operation condition, so that the operation stability is improved.

The invention belongs to the field of new energy and intelligent power grids, and discloses a light storage and charging four-function system and method based on edge intelligent regulation, and the system comprises a cloud collaborative optimization layer, an edge intelligent control layer, and a multi-source terminal autonomous layer. By adopting the system, the panoramic perception and millisecond-level fault autonomy of the equipment operation state are realized, and the problems of unobservable and unmeasurable problems are solved; the power regulation precision and the fault removal efficiency are improved through protocol unification and edge intelligent regulation and control, and the problems of non-regulation and non-control are solved; by means of a cloud optimization-edge execution-terminal autonomy cooperative mechanism, the impact of photovoltaic fluctuation on a power grid is stabilized, voltage out-of-limit and harmonic pollution are suppressed, the relay protection reliability is enhanced, meanwhile, the hardware cost and the single-point failure risk are reduced, and the four-capability requirements of observability, measurability, adjustability and controllability are comprehensively met.

The invention relates to a method for calculating an optimal SOC value of an optical energy storage system taking photovoltaic prediction errors into account, and belongs to the technical field of optical energy storage systems. According to the method, SOC management of the optical storage system is divided into two time sequences, and a long-term optimization model and a short-term optimization model are obtained; operating the long-term optimization model and the short-term optimization model; the current SOC state output by the short-term optimization model is fed back to the long-term optimization model and the like for information interaction; and the long-term optimization model is operated again regularly according to the latest photovoltaic prediction and scheduling requirements. The combined optimization method is adopted, the adaptive capacity of the optical storage system to photovoltaic prediction errors can be remarkably improved, and stable and efficient operation of the system can be ensured; the robustness is improved, the efficiency is optimized, the service life of the battery can be prolonged, and the applicability is wide; according to the invention, the stability, the efficiency and the economical efficiency of the optical storage system can be obviously improved, and an effective solution is provided for accessing the high-proportion renewable energy to the power grid.

The invention belongs to the technical field of optical storage and charging collaborative interaction, and particularly relates to a microgrid regulation capability evaluation system and method based on optical storage and charging collaborative interaction, and the system comprises a multi-source data fusion module, a dynamic evaluation module, a collaborative optimization module, a digital twin verification module and an output regulation and control module. The signal output end of the multi-source data fusion module is connected with the dynamic evaluation module, the signal output end of the dynamic evaluation module is connected with the collaborative optimization module, and the signal output end of the collaborative optimization module is connected with the digital twin verification module. And the signal output end of the digital twin verification module is connected with the output regulation and control module. And the mixed spatio-temporal data fusion technology reduces the synchronization error of the multi-source data. And a dynamic evaluation-optimization combined solving method is adopted, so that the calculation complexity is reduced. According to the event-driven rolling optimization mechanism, the photovoltaic output volatility and the load abrupt change rate are defined as trigger threshold values, only the affected subsystem is re-evaluated, and the calculation amount is reduced.

The invention discloses a method and system for improving the energy utilization rate by utilizing light storage resource prediction, and the method comprises the steps: collecting irradiance temperature historical output data and load consumption feature data through a distributed sensor and an edge gateway, processing time sequence features through a sequence prediction model, and integrating multi-dimensional input through the fusion of an integrated learning model; a future prediction result of photovoltaic output and load demand is obtained; further acquiring real-time parameters of the charge state and the charge and discharge efficiency of the energy storage equipment, constructing an energy storage full life cycle state evaluation model to calculate dynamic indexes, and determining an available capacity boundary; and if the boundary exceeds a preset threshold value, peak-valley period characteristics are extracted, and an optimal strategy sequence including photovoltaic peak charging and load peak discharging is generated by adopting an intelligent optimization algorithm in combination with power grid peak-valley electricity prices and carbon emission constraints. According to the invention, the photovoltaic consumption rate is obviously improved, the operation cost is reduced, the carbon emission requirement is met, and intelligent and efficient management of the optical storage system is realized.