1099 results about "Light storage" patented technology

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 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 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 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 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 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 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 a power system random scheduling method based on wind-solar joint probability distribution and double-layer dynamic optimization. The method comprises the following steps: firstly, constructing a wind and light output joint probability distribution model by adopting nonparametric kernel density estimation and a Frank Copula function so as to accurately describe space-time correlation; secondly, generating a wind-solar combined output scene through Monte Carlo simulation, and performing reduction by adopting a k-means + + clustering algorithm to obtain a typical scene set; secondly, establishing a double-layer optimization model, wherein the upper layer takes the minimum net load variance as a target to coordinate wind and light storage output stabilizing fluctuation; and the lower layer optimizes the output of the traditional unit by taking the minimum operation cost of the system as a target. Finally, a dynamic climbing constraint mechanism is introduced, the net load fluctuation standard deviation output by the upper layer is converted into a dynamic constraint threshold value of the climbing rate of the lower layer unit, collaborative optimization of economical efficiency and stability is achieved, and the dispatching robustness of the high-proportion renewable energy power system is remarkably improved.

The invention relates to the technical field of light storage and charging integration, in particular to an intelligent micro-grid power supply and load balance control method based on light storage and charging integration, which comprises the following steps of: synchronously acquiring a photovoltaic string output current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance curve, and outputting a mismatch characteristic frequency spectrum; the mismatch characteristic frequency spectrum marks a resonance risk frequency band; and based on the mismatched characteristic frequency spectrum, generating a light-storage-charging power coordination instruction set through a harmonic suppression collaborator, including instructing a photovoltaic string to start an active damping mode in a resonant frequency band and dynamically distributing differential switching frequency of a charging pile. According to the method, a mismatch characteristic frequency spectrum model fusing a photovoltaic current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance characteristic is constructed for the first time, a resonance risk frequency band is analyzed and judged through frequency domain coupling, and feedforward identification of inter-harmonic excitation conditions of multi-source high-frequency conversion equipment is realized; the method has higher system level adaptability and harmonic risk positioning precision.

The invention relates to the technical field of intelligent power grids, in particular to an optical storage agricultural micro-grid operation optimization method and system, and the method comprises the steps: constructing an optical storage agricultural greenhouse micro-grid, and constructing a robust optimization scheduling model under the photovoltaic output uncertainty to carry out robust optimization scheduling; based on the scheduling scheme of robust optimization scheduling, constructing a micro-grid load model for the light storage agricultural greenhouse; on the basis of the load demand in the micro-grid load model, a cost-benefit optimization model under two operation modes of autonomous investment construction and energy performance contract is constructed, and the cost-benefit is calculated on the basis of the cost-benefit optimization model under the actual operation mode, so that the energy storage capacity configuration and the operation strategy are subjected to collaborative optimization. And constructing an energy storage system capacity configuration and operation optimization model, and finally determining an energy storage configuration scheme and a multi-time scale scheduling strategy. According to the invention, the problems of photovoltaic output uncertainty, single investment operation mode and insufficient economical efficiency optimization of the existing system are effectively solved.

The invention discloses a distributed light storage and charging resource cooperative regulation and control method and system, and the method comprises the steps: collecting historical operation data and equipment parameters, and carrying out the preprocessing, and obtaining a standardized data set; establishing a tree embedding model, respectively constructing characteristic / dynamic characteristic / response models of a photovoltaic device, an energy storage device, a charging facility and an adjustable load, and forming a distributed resource characteristic model set; identifying various resource energy storage behavior modes, and mapping each model into a generalized energy storage equivalent model to obtain a comprehensive generalized energy storage model; a resource schedulable potential matrix is constructed, potential is calculated after dimension reduction processing, and an evaluation report is generated; a multi-target regulation and control optimization function is constructed, distributed resource aggregation is modeled, a collaborative regulation and control scheme is obtained by combining hierarchical optimization of the optimization function and an aggregation characteristic model, then the collaborative regulation and control scheme is decomposed into specific control parameters of all resources, and a distributed resource regulation and control instruction set is formed. According to the invention, efficient cooperative regulation and control of distributed resources are realized.

The invention relates to a multi-data center elastic scheduling method, which comprises the following steps of: establishing a data center polymer (DCA) to integrate the space-time regulation potential of a dispersed data center, establishing a comprehensive model covering dynamic migration of a working load, power consumption regulation and control of a DVFS (Distributed Valve File System) and collaboration of an optical storage system, and adopting a double-layer elastic scheduling architecture, energy storage charging and discharging, photovoltaic output and other hyperopia actions are optimized to improve long-term decision robustness; and the lower layer solves short view actions such as server scheduling and load distribution in real time based on mixed integer linear programming to realize instant cost optimization, so that power-computing power collaborative scheduling optimization of the data center is realized. According to the method, flexible scheduling of time delay sensitive type and time delay tolerant type workloads is combined, the cooperative effect of an energy storage system and photovoltaic power generation is utilized, the power use efficiency and economical efficiency of the data center are optimized, and dynamic participation of multiple data centers in the power market is achieved.

The invention belongs to the technical field of power grid systems, and particularly relates to a cooperative scheduling method for a water-electricity-heat combined supply negative carbon building light-storage direct-flexible energy microgrid group. Comprising the following steps: constructing a negative carbon building light storage direct-flexible energy microgrid group mathematical model; wherein the mathematical model comprises a generator constraint model, an external power grid and micro-grid group exchange power constraint model, a power balance constraint model, a peak-valley difference constraint model, a storage battery constraint model, a phase change heat storage system operation constraint model and a water supply system operation constraint model; constructing a target function of the scheduling model by taking the minimum total operation cost of the negative carbon building light storage direct-flexible energy microgrid group in a scheduling period as a target; wherein the total operation cost comprises electric power procurement cost, carbon emission cost, energy storage cost, fuel cost and peak-valley difference punishment cost. Through linkage of the electricity storage system, the heat storage system and the water storage system, the utilization rate of renewable energy sources and the flexibility of the system are maximized.

The invention relates to the technical field of energy management, and provides a park light storage and charging energy distribution control method and system, and the method comprises the steps: transmitting an instruction to an energy storage system in an emergency, enabling the energy storage system to release an electric energy pulse to a micro-grid at the maximum available power within a preset time, and enabling the micro-grid to be in the maximum available power; providing time buffer for the power reduction process of the load units in the park; after the power reduction instruction is executed, identifying response characteristics of each load unit in the park to the power reduction instruction, and grading each load unit according to the response characteristics to obtain grading information; during the time buffering period, according to the grading information of the load units, a grading decreasing load reduction plan is formulated and executed, and a reduction instruction is issued in a peak shifting manner, so that the overall load is smoothly reduced; and continuously monitoring the power quality of the micro-grid, and finely adjusting the output of the energy storage system according to the power quality of the micro-grid. The method has the effect of improving the stability and reliability of light storage and charging in the park.

The invention belongs to the technical field of energy management, and particularly relates to a light storage direct current flexible load self-evolution cooperative scheduling method, system and equipment, and the method comprises the steps: constructing a parameterized energy utility curve, quantifying the comprehensive utility of flexible load response in energy efficiency, comfort and equipment loss, and calculating the unit power marginal utility as the flexibility; establishing a multi-target collaborative scheduling model considering the time-varying carbon intensity, the electricity price and the utility curve, and solving by adopting a model predictive control and reinforcement learning mixed strategy; static and dynamic data are fused to construct a knowledge graph, and flexibility is predicted and cross-scene migration is realized through a sequence diagram neural network; and cooperatively optimizing a knowledge graph prediction result and a scheduling instruction through a Lagrangian relaxation method to form a self-evolution closed-loop control system. According to the method, flexible load refined modeling, carbon perception economic optimization scheduling and system adaptive learning are realized.

The invention discloses an optical storage and charging integrated micro-grid system, relates to the technical field of optical storage and charging integration, and aims to solve the problem that the storage and charging effect of optical energy is unstable and unsafe. Different control instructions are generated according to different scenes, various conditions in the operation process of the micro-grid can be comprehensively covered, fine management and control of the micro-grid are achieved, a corresponding load distribution strategy is formulated, the system can be flexibly adjusted according to actual conditions, the electric energy utilization efficiency and the stability of the system are improved, and the system is suitable for popularization and application. When the photovoltaic power generation power is insufficient, energy storage discharging and photovoltaic electric energy combined power supply are started, reasonable storage and flexible allocation of electric energy are achieved, the power supply reliability and flexibility of the system are enhanced, an electric energy dynamic scheduling strategy comprises four scenes of charging, discharging, balance and safety, flexible switching can be achieved according to the actual operation condition, and the power supply efficiency is improved. The method adapts to operation requirements of the micro-grid under different working conditions, and has relatively high flexibility and adaptability.

The invention discloses a distributed light storage and charging integrated system, which comprises a photovoltaic power generation unit, an energy storage unit, a charging unit, a power grid interface unit, an energy management controller and a direct current bus, the photovoltaic power generation unit comprises a DC / DC converter, and the photovoltaic power generation unit is connected with the DC bus through the DC / DC converter; the energy storage unit comprises a lithium ion battery pack, a flywheel energy storage device, a bidirectional DC converter and a flywheel controller. The lithium ion battery pack and the flywheel energy storage device are connected to the DC bus through the bidirectional DC converter and the flywheel controller respectively. The charging unit comprises at least one direct-current quick charging pile, at least one alternating-current slow charging pile and a DC / AC inverter, the direct-current quick charging pile is directly connected with the direct-current bus, and the alternating-current slow charging pile is connected with the direct-current bus through the DC / AC inverter; the power grid interface unit comprises an AC / DC converter and is connected with the DC bus through the AC / DC converter. According to the invention, through collaborative design of a modular hardware architecture and an intelligent time-sharing strategy, comprehensive improvement of economy, reliability and functionality of the system is realized.

The invention relates to the technical field of intelligent management of new energy facilities, in particular to an intelligent management method and system for a rural light storage and charging station combined with point cloud data. The method comprises the following steps: acquiring point cloud data of a temporary deployment site; performing point cloud site modeling according to the point cloud data to obtain a point cloud site model; performing point cloud site feature extraction according to the point cloud site model to obtain point cloud site feature data; according to the point cloud site feature data, performing deployment optimization region calculation to obtain deployment optimization region data; and performing intelligent deployment simulation according to the deployment optimal area data to obtain intelligent deployment data. According to the efficient deployment management method combining point cloud space perception and AI intelligent optimization, the construction and operation quality of the rural light storage and charging station 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 the technical field of power station cooperative scheduling, and discloses a cooperative scheduling method and system for a photovoltaic storage and charging integrated power station, and the method comprises the steps: calculating an electrical load demand curve and a photovoltaic power generation curve of a next scheduling period based on historical power consumption data and weather forecast data; analyzing the electrical load demand curve and the photovoltaic power generation curve of the next scheduling period based on the multi-time scale optimization scheduling model, and making a first energy storage scheduling plan; in the next scheduling period, performing energy storage scheduling on the energy storage device based on the first energy storage scheduling plan, and in the energy storage scheduling process, performing energy storage scheduling optimization based on a preset time interval, and setting a sub-scheduling period; and collecting real-time power consumption data and real-time meteorological data of the current sub-scheduling period, inputting the real-time power consumption data and the real-time meteorological data into the multi-time scale optimization scheduling model, and outputting a second energy storage scheduling plan of the sub-scheduling period, and the method realizes cooperative scheduling and intelligent cooperative control of the photovoltaic storage and charging integrated power station.

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 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.

The invention discloses a security event traceability and response management platform suitable for a wind and light storage station. The platform comprises a station edge layer and a cloud middle platform layer. The station edge layer comprises an acquisition and isolation unit, a time synchronization and evidence shaping unit and a credible evidence storage and equipment snapshot unit; the evidence storage module is used for underlying data acquisition, time synchronization, evidence shaping and credible evidence storage to form original event data and an encrypted evidence chain; the cloud middle platform layer comprises a cross-domain causal atlas and root cause analysis engine, a control physical consistency verification model, a hierarchical response and energy storage security state control unit, a drill and strategy verification unit and a redisk and adaptive optimization unit; the method is used for cross-domain causal analysis, control consistency verification, hierarchical response decision and strategy redisk optimization. Unified acquisition, credible recording and cross-domain traceability of security events are realized, event sources and propagation paths can be accurately identified in a complex operation environment, and closed-loop control of security response is supported.

The invention discloses a solar photovoltaic power generation and energy storage integrated communication machine room and an energy management and multi-mode refrigeration control method thereof, and belongs to the field of solar photovoltaic power generation green low-carbon communication machine rooms. The communication machine room takes an optical storage all-in-one machine and a unified direct-current bus as a core, photovoltaic power is connected to the direct-current bus through DC / DC, stored energy is connected to the direct-current bus through bidirectional DC / DC, commercial power is connected to the direct-current bus through AC / DC, a bus voltage stabilizing module is configured, and photovoltaic power supply priority, residual power charging and gap energy complementation are achieved. The energy management method comprises the steps of collecting power and a charge state to generate a state quantity, distributing the power according to priorities and issuing a control instruction. According to the refrigeration control method, the refrigeration mode or the mixed mode of the heat pipe and the compressor is switched according to the outdoor temperature threshold value and the refrigeration requirement, the three-way valve, the compressor and the fan are cooperatively controlled, and hysteretic and minimum retention time constraints are adopted to restrain frequent switching. According to the scheme, the renewable energy proportion and power supply continuity are improved, refrigeration energy consumption is reduced, and temperature control of the machine room is kept stable.

The invention relates to the technical field of power distribution network optimization, in particular to a power distribution network voltage dynamic optimization control method, system and device and a storage medium, and the method comprises the steps: judging a mode interval in real time, and judging whether the system operates in a normal state or a fault state according to voltage fluctuation; calculating the photovoltaic residual capacity and the real-time charge state of the energy storage battery; according to the operation state of the system, judging the action state of the energy storage system and the photovoltaic operation mode, and calculating the total reactive power demand of the power distribution network system; on the basis of an improved TOPSIS algorithm, a power instruction is calculated by considering the voltage variation of each node in a power distribution network, the photovoltaic power regulation capability, the charge state of an energy storage system, the available residual capacity of energy storage and the voltage variation of a dominant node caused by unit power, and the power instruction is distributed to an optical storage node. According to the invention, power dynamic distribution under dual modes of a normal state and a fault state is set to ensure stable operation of voltage; and the power distribution network always operates in an optimal state.

The invention discloses an optical storage and charging dynamic power distribution control method and system based on AI prediction, and the method comprises the steps: collecting photovoltaic, energy storage, charging, power grid and environment multi-dimensional operation data through a sensor network, and obtaining a historical and real-time data set through preprocessing; training an AI joint prediction model by using the historical data set, and outputting a photovoltaic output and charging load demand prediction result; inputting system operation constraint condition parameters to delimit an optimization boundary; solving a power instruction reference value of each power module through a multi-objective optimization algorithm in combination with a prediction result and a constraint condition to realize global optimal distribution; and correcting the reference value based on the hardware operation parameters and the deviation between the predicted value and the real-time value, issuing an instruction, monitoring operation, and triggering fault protection. The method effectively solves the problems that a traditional system is rigid in power dispatching, low in photovoltaic consumption rate, large in energy storage life loss, remarkable in power grid impact, insufficient in prediction precision, high in energy transmission loss and weak in multi-target coordination.

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.