672 results about "Grid system" patented technology

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

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

The embodiment of the invention provides a low-altitude digital management method and device based on an urban space-time grid, and the method and device achieve the precise division of an airspace through a plurality of layers of grid units by creatively building a unified urban space-time reference grid system. And designing a space-time grid data assignment model, processing multi-source heterogeneous data by using a deep neural network and a space-time convolutional layer, and realizing intelligent mapping of grid features through an attention mechanism. And constructing an airspace situation awareness model based on a graph neural network, and capturing a space-time dependency relationship among grid units in combination with a message passing mechanism to realize accurate prediction and hierarchical management and control of an airspace state. According to the method, the defects of the traditional technology in the aspects of airspace division, data processing, situation awareness and the like are effectively overcome, and the accuracy and practicability of urban low-altitude digital management are remarkably improved.

The invention relates to the technical field of energy storage configuration, in particular to an energy storage configuration method and system for a photovoltaic off-grid system, and the method comprises the following steps: obtaining illumination intensity and component output change, recognizing a slope abrupt change section, marking the change trend, extracting the difference between the energy storage change and the power generation direction, and grouping a delay response time period; and extracting operation paths, sorting and matching the operation paths to a response section, setting a multiplying power change direction, synchronizing unselected path states, sequentially arranging adjustment instructions, and writing the adjustment instructions into a control channel to obtain a photovoltaic off-grid energy storage configuration scheme entry set. According to the method, a change section is constructed by identifying sudden change of a power slope, response lag adjustment is set by combining energy storage and power generation direction comparison, rate matching is carried out, charge state and discharge fluctuation double indexes are introduced through path sorting, path selection precision is improved, rate setting is adjusted in parallel according to priorities, and an adjustment instruction is generated in cooperation with time mapping. And the energy storage response and control synchronism in the sudden change scene is enhanced.

The invention relates to the technical field of micro-grid energy optimization, in particular to a distributed federal reinforcement learning-based hydrogen-containing micro-grid energy scheduling method, which comprises the steps of constructing a multi-region hydrogen-containing micro-grid system model, designing a state space, an action space and a reward function of an intelligent agent, constructing an Actor-Critic network and an experience pool, and completing environment initialization. The intelligent agent inputs the operation state of the equipment into the Actor network, updates the state of the equipment according to the output action, verifies the constraint and outputs a reward value; tuples are extracted from the experience pool to update local network parameters, and the exploration rate is updated regularly; when a federation interaction period is reached, exchanging Critic network parameters and updating federation parameters; and when the training round arrives, outputting an equipment operation plan, deploying the model to the local hydrogen-containing micro-grid in the island mode, and outputting an equipment output value. According to the scheme, strategy sharing and learning collaboration are realized through neighborhood collaboration and local communication among the regional intelligent agents, so that dependence on a central node is avoided.

The invention relates to the field of clean energy, and provides a distributed secondary control method for an island micro-grid system based on event triggering, which comprises the following steps: S1, connecting adjacent distributed units in an island micro-grid through a local communication link, and constructing a distributed control architecture to eliminate dependence on a central controller; s2, triggering communication of the local communication link based on a preset threshold value, and transmitting a secondary control signal when the system state deviation exceeds the threshold value; s3, performing cooperative calculation on the secondary control signal by using the distributed control architecture, adjusting frequency and voltage deviation, and shielding original power data; and S4, connecting a newly added distributed unit to the distributed control architecture through the local communication link, thereby reducing high communication burden and single-point fault of centralized control of the island microgrid.

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

The invention provides an indoor navigation and path planning method, device and equipment. Comprising the steps that path planning request parameters are acquired, and the request parameters at least comprise a starting point, an ending point, a time context, a user portrait and a scene template of path planning; generating a runtime snapshot reflecting the traffic state at the current moment in combination with a pre-constructed static grid system, a knowledge graph and a dynamic grid system generated in real time; according to an indoor structure mask and guide identification information in the static grid system, generating a search domain limit of path planning on the runtime snapshot; judging whether the starting point and the ending point are on the same floor; performing path search by adopting a same-layer search strategy or a cross-layer search strategy; and dynamically injecting constraints to the result of the path search and calculating the path cost to generate a path plan. According to the embodiment of the invention, the indoor space can be uniformly expressed, the multi-source dynamic information can be fused in real time, and the path planning scheme can be quickly generated, so that the navigation requirement in a modern complex indoor scene can be met.

The invention discloses a micro-grid system based on a direct current bus and a control method, the micro-grid system comprises the direct current bus, an energy management unit and a plurality of power conversion modules, when the micro-grid system is in an independent adjusting mode, each power conversion module controls the direct current bus to operate according to bus operation parameters of the direct current bus; and when the micro-grid system is in a centralized regulation mode, the energy management unit issues a control instruction to coordinate the output power and / or the input power of each power conversion module. According to the technical scheme, a unified direct current bus framework is constructed, the photovoltaic energy unit, the energy storage energy unit, the commercial power energy unit, the direct current load and other loads are directly connected to the direct current bus, direct current direct supply and direct current direct charging are achieved, the energy link structure is simplified, the power conversion link is reduced, the overall energy efficiency of the system is improved, and the system cost is reduced. And the phenomena of multiple energy conversion and energy loss caused by dependence on an alternating current bus in the prior art are avoided.

The invention discloses a high-order direct-current micro-grid system multi-loss synchronous convergence modeling method based on an improved physical information neural network. The method comprises the steps that S1, a physical information neural network model is initialized; s2, calculating physical loss and updating parameters of the physical information neural network model; s3, reflecting parameters of the physical information neural network model around physical optimization result points; s4, calculating data loss by using the reflected parameters and updating parameters of the physical information neural network model; s5, using the parameters obtained in S2 and S4 to update the parameters of the physical information neural network model through the iterative operation of Douglas-Rachford splitting; and S6, iterating the parameter updating process to solve the physical information neural network model. Different from a traditional physical information neural network which is difficult to converge for high-order system training, the method can realize synchronous convergence of physical loss and data loss in high-order direct-current micro-grid system fitting, and has important significance for mastering an internal operation rule of a direct-current micro-grid system in real time and establishing a physically interpretable data driving model.

The invention discloses a diesel-storage intelligent micro-grid system. A diesel generator charges an energy storage unit or cooperatively supplies power when power supply is insufficient; the battery energy storage unit stores electric energy and supplies power to a load; the micro-grid energy management system dynamically coordinates the output proportion, and a harmonic suppression module suppresses harmonic interference; the load prediction module predicts load power change; the transient response control module deals with transient power impact; the power distribution system is connected to a main power supply and is switched to a diesel generator power supply mode when power supply of the main power supply does not meet requirements; when the diesel generator supplies power, the bidirectional converter converts electric energy into charging current of the energy storage unit and inhibits harmonic interference; when the energy storage unit supplies power, the direct current is converted into the alternating current required by the load. According to the invention, seamless switching of multiple energy sources, active stabilization of power fluctuation, flexible conversion of electric energy forms, optimization of energy distribution efficiency and comprehensive improvement of the survivability of the micro-grid can be realized.

The invention discloses a heterogeneous graph neural network-based microgrid cooperative scheduling prediction and security check method. The method comprises the steps of constructing a heterogeneous graph model; different micro-grid operation scene graphs are generated through analogue simulation; solving an optimal collaborative scheduling strategy vector for each scene graph to serve as an optimal scheduling label, generating a training data set of a'scene graph-optimal scheduling label 'data pair, and completing offline training of the graph neural network model; dynamically generating an instantiated heterogeneous graph; taking the instantiated heterogeneous graph as the input of the trained graph neural network model, carrying out first forward reasoning, and outputting a predicted collaborative scheduling strategy vector; and injecting a virtual fault, generating a virtual fault heterogeneous graph, inputting the virtual fault heterogeneous graph and the predicted cooperative scheduling strategy vector into the trained graph neural network model, carrying out second forward reasoning, predicting the state of the faulty micro-grid system, obtaining the predicted fault node voltage, and carrying out safety check on the predicted cooperative scheduling strategy vector.

The invention discloses an off-grid new energy hydrogen production start control method, system, device and medium, and relates to the technical field of new energy hydrogen production control and micro-grid operation management, and the method comprises the steps: employing energy storage zero-to-boost control and anti-interference communication to establish a bus voltage based on an off-grid system architecture; the output power of the wind turbine generator is adjusted through a variable pitch power following strategy based on short-term wind speed prediction, the power distribution proportion of the wind turbine generator and the energy storage device is dynamically adjusted by adopting an improved NSGA-II multi-target optimization algorithm in combination with the equipment health state factor, and the power output path and the response rate of the equipment are optimized; electrolytic hydrogen production is carried out by adopting electrolytic tank pre-starting condition inspection, stepped power rising and stepped starting of grid-connected transition in cooperation with closed-loop temperature flow regulation and a PID pressure management strategy, and black-start rollback and self-healing control are executed. According to the method, the stable construction of the bus voltage is realized through the boost control from zero energy storage, and the current impact in the starting process is effectively inhibited.

The invention belongs to the technical field of load prediction, and discloses an off-grid micro-grid system load prediction method and system based on a neural network. The method comprises the following steps: constructing an equipment fault diagnosis model, an energy scheduling model and a load prediction model by using a neural network algorithm; carrying out data acquisition and preprocessing to obtain preprocessed real-time area monitoring data; performing equipment fault diagnosis by using the equipment fault diagnosis model; if the real-time equipment fault diagnosis result is that a fault exists, entering an energy scheduling step, otherwise, entering a load prediction step; performing energy scheduling by using the energy scheduling model, executing the obtained real-time energy scheduling scheme, and returning to the data acquisition step; and performing load prediction by using the load prediction model. According to the method, the problems of data island, insufficient model generalization ability, limited fault diagnosis precision and efficiency, insufficient energy scheduling strategy optimization and poor load prediction precision and robustness in the prior art are solved.

The invention provides an off-grid synthetic ammonia park electrolytic bath column level-energy storage-hydrogen storage collaborative optimization method based on rolling optimization, and relates to the technical field of energy storage and hydrogen storage collaboration. The method comprises the following steps: constructing an off-grid system architecture comprising wind-solar power generation, a plurality of rows of electrolytic cells, energy and hydrogen storage equipment and a synthesis ammonia device; a multi-device collaborative optimization strategy based on an eight-hour rolling time domain is designed, the system operation state is divided into high, medium and low working conditions through a working condition recognition mechanism, a current device operation scheme is actively adjusted according to wind and light power information of a next scheduling period, and collaborative regulation and control of energy storage and hydrogen storage are achieved. Compared with a traditional scheduling strategy, the collaborative optimization strategy provided by the invention improves the consumption rate of renewable energy sources of the system, reduces abandoned wind and light, improves the yield of synthetic ammonia, and verifies the effectiveness of the strategy.

The invention discloses a micro-grid optimization scheduling method based on an improved Loueer fox algorithm, relates to the technical field of micro-grid operation, and realizes collaborative optimization scheduling of a distributed power supply, an energy storage device, a load and the like in a micro-grid system by simulating various perception and behavior strategies of the Loueer fox. Aiming at the demand of distributed power supply system micro-grid optimization scheduling, the micro-grid system operation economy is taken as a main target, a function corresponding to a micro-power supply scheduling scheme is constructed, improvement is carried out based on an RFO algorithm, and a quantum fractal annealing disturbance strategy, a hyperbolic tangent self-adaptive olfactory disturbance strategy and a Riemannian metric accumulation disturbance strategy are introduced, so that the optimal scheduling of the micro-power supply system is realized. Through the fusion of the triple innovation strategies, the defects of poor population diversity, easy premature convergence, insufficient balance capability, low precision and the like when the original RFO algorithm and the traditional intelligent algorithm are used for solving the complex optimization problem are solved, and the efficient solution of the micro-grid optimization scheduling scheme is realized.

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 method, a system, equipment and a medium for evaluating stable operation capability of an isolated network of a regional power grid. The method comprises the following steps: acquiring related data of an isolated network system; based on related data of the isolated network system, initial equivalent inertia and initial power deviation are obtained through calculation; based on the frequency deviation value of the isolated network system, operating equipment of the isolated network system is adjusted, and equivalent inertia and power deviation are calculated and updated; calculating a disturbance frequency change rate according to the equivalent inertia and the power deviation; and judging a corresponding relation between the power deviation and the disturbance frequency change rate, and obtaining an evaluation result of the stable operation capability of the isolated network system. According to the isolated network stability quantitative evaluation method constructed based on the frequency change rate, only two core parameters of the system inertia and the new energy proportion are needed, complex modeling and time-consuming simulation are not needed, and the evaluation efficiency is improved through a dynamic parameter updating mechanism of high-frequency generator tripping and low-frequency load shedding in combination with a simplified frequency response model; whether the isolated network is endangered to collapse is determined in real time, and an emergency control decision basis is provided for power grid personnel.

The invention discloses an independent micro-grid optimal scheduling method based on multi-time scale source-load-storage coordination, and belongs to the technical field of power system optimal scheduling. The output volatility of renewable energy sources (such as wind energy and solar energy), the randomness of load demands and the adjusting capacity of an energy storage system and demand response resources are comprehensively considered, and the economical efficiency, reliability and renewable energy source consumption rate of the system are comprehensively improved through multi-level optimization scheduling. The method is suitable for an independent micro-grid system in remote areas (such as industrial and mining areas, islands or frontier sentries), aims to solve the technical problem that traditional single-time-scale scheduling cannot adapt to variable working conditions, and provides an innovative solution for low-carbon, efficient and reliable energy supply.

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 forest fire carbon emission and carbon sequestration collaborative quantification method and system, and the method comprises the steps: obtaining forest multi-dimensional data including fire point data, and constructing a multi-scale collaborative database; constructing a carbon source-carbon sink dynamic coupling model according to the multi-scale collaborative database; obtaining a forest multi-remote sensing index collaborative weight, and correcting the carbon source-carbon sink dynamic coupling model; and generating a forest spatialization decision map based on the multi-scale collaborative database and the carbon source-carbon sink dynamic coupling model. According to the technical scheme provided by the invention, by fusing forest multi-scale data, a nationwide unified grid system is constructed, and accurate quantification of large-scale forest fire carbon emission and carbon sequestration is realized; a carbon source-carbon sink dynamic coupling model is provided, and cooperative diagnosis of multiple remote sensing indexes is combined, so that the monitoring precision of the fire influence is remarkably improved; and finally, a spatial decision map of forest carbon emission and carbon sequestration is generated, carbon sink protection area delimitation is supported, and a scientific basis is provided for policy formulators.

The invention relates to a numerical simulation method for multiphase fluid stratum seepage, which belongs to the technical field of numerical simulation and comprises the following steps: S1, generating an unstructured grid by adopting a mode of combining a leading edge propulsion method and Delaunay triangulation; s2, performing dynamic adjustment through an adaptive grid encryption technology according to the change of the seepage physical quantity; s3, establishing a dynamic phase change model, and obtaining a function of representing the phase change rate as temperature, pressure and concentration of each phase; s4, the mass and energy change of each phase in the phase change process is calculated in real time through a kinetic equation; s5, processing dynamic evolution of a phase interface by adopting a phase field method, and describing distribution conditions of different phases through phase field variables; and S6, coupling the grid system obtained by dividing the multi-scale unstructured grid with the dynamic phase change model through a numerical solution algorithm. According to the method, the complex geometrical shape of the stratum can be better fitted through multi-scale unstructured grid division, and then the change of the phase change process along with time and space can be tracked in real time through the dynamic phase change model.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention discloses an artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method and system, and the method comprises the steps: obtaining multi-source geographic space data of a target region, uniformly fusing the multi-source geographic space data into a grid system with a preset resolution, and carrying out the calculation of the multi-source geographic space data based on a space weighted distribution model, the statistical yield of provincial agricultural products is distributed to each grid of a grid system, and the theoretical resource quantity and the high-precision spatial distribution diagram of the agricultural wastes are obtained in combination with a grass-grain ratio database. Method for determining carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths through dynamic accounting model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenes based on artificial intelligence model according to carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths, and planting front-end crops and structure and space layout is optimized. A quantifiable and visual scientific decision-making tool is provided for regional low-carbon agricultural planning, and the accuracy and feasibility of an emission reduction strategy are remarkably improved.

The invention belongs to the technical field of gas storage building, and particularly relates to an aquifer gas storage building displacement front identification method. According to the method, a water-bearing stratum gas storage model grid system is constructed, the displacement front edge of the water-bearing stratum gas storage model is positioned on the basis of interface energy gradient analysis, and the dynamic front edge boundary is calibrated with the maximum gradient 10% threshold value by calculating the interface energy and the three-dimensional gradient of each grid; and further quantizing the front edge complexity by adopting a coarse-fine grid fractal dimension method, and dividing the front edge into a stable region, a transition region and a risk region through quartile. According to the method, the dynamic evolution characteristics of the gas-water interface are directly represented through an interface energy gradient mechanism, the high-risk gas channeling region is quantitatively recognized in combination with the fractal dimension, and technical support is provided for reservoir building optimization and gas channeling prevention and control of the heterogeneous aquifer gas storage.

The invention provides a power cooperative control method, device and system based on load removal and a medium. The method is applied to a micro-grid configuration flexible interconnection device for micro-grid fault protection. After the fault is positioned, a VSG low-voltage ride-through strategy is adopted to carry out current limiting, an importance quantification model is constructed for loads, and according to a real-time operation condition, a shearable load with the minimum influence on the system is screened to carry out priority removal; and dynamic power transmission adjustment of the flexible interconnection device is synchronously triggered, a micro-grid power flow path is reconstructed, and finally a fault is removed based on a current limiting and directional element protection method. According to the invention, system protection is realized by fusing intelligent load removal and flexible power adjustment, and the fault isolation speed and the stability of the microgrid system are effectively improved.

The invention belongs to the technical field of power distribution network operation optimization, and particularly relates to a multi-microgrid-containing regional power distribution network self-discipline collaborative operation optimization method which comprises the following specific steps: S1, data acquisition and preprocessing; s2, establishing a model; s3, a self-discipline cooperative control strategy; s4, solving by an optimization algorithm; and S5, real-time monitoring and adjustment are carried out. And through self-discipline cooperative operation optimization, an information interaction and cooperation mechanism among the micro-grids is constructed, and resource sharing and advantage complementation of the multi-micro-grid system are realized. A distributed collaborative optimization algorithm and a self-discipline control strategy are adopted, and dependence on centralized communication is reduced. The uncertainty of output and load demand of the distributed power supply is fully considered by utilizing technologies such as model predictive control and stochastic optimization. According to the scheme, a plurality of optimization objectives such as economy, environment and reliability are comprehensively considered, and the optimal operation strategy is solved by adopting a multi-objective collaborative optimization algorithm.

The invention provides a cooperative control method for park-level multi-microgrid participation in power grid interaction, and the method comprises the steps: obtaining power grid real-time data of a power grid platform and multiple microgrids, and each microgrid comprises a distributed power supply, an energy storage unit and a load; obtaining a power grid operation state according to the power grid real-time data, wherein the power grid operation state comprises a grid-connected mode and an off-grid mode; performing energy management analysis based on the power grid operation state and the power grid real-time data, and generating a control instruction; based on the control instruction, carrying out frequency voltage stability control on bottom layer equipment of the multiple microgrids; and making an electric energy transaction strategy based on the control instruction and the multiple microgrids. According to the invention, stable control in the micro-grid is realized, and the economy, stability and operation efficiency of the multi-micro-grid system are improved.

The invention discloses a coastal zone planning method and system based on MaXent and hotspot analysis, and relates to the technical field of coastal zone planning. Comprising the following steps: S1, collecting environment variable data, species distribution data and development intensity data, constructing a unified space-time grid system, and realizing time sequence expression of the data through data preprocessing; s2, constructing a quarterly time sequence maximum entropy model, and performing time sequence evaluation on each grid habitat state to form a habitat evolution type; s3, identifying time sequence significant hot spots and hot spot persistence and intensity features, constructing ecological development relationship comprehensive representation, and identifying ecological development conflict types; and S4, spatially superposing the habitat evolution type and the ecological development conflict type to form a self-adaptive access rule, and generating differentiated planning suggestions according to regions. The problem that planning lacks dynamic adaptability due to the fact that coastal zone habitat suitability and development intensity depend on static time point data for a long time and a time sequence evolution rule cannot be recognized is solved.