272 results about "Grid optimization" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention provides a micro-grid dispatching method based on a hybrid genetic particle swarm optimization algorithm. The method comprises the following steps: constructing a micro-grid system comprising a photovoltaic cell PV, a fan WT, a diesel generator DE and a micro gas turbine MT; based on the micro-grid system, a multi-objective optimized micro-grid optimization scheduling model is constructed, an objective function of the optimization scheduling model is set to be cost for minimizing the micro-grid in a grid-connected mode, and the cost comprises operation cost and environment cost; and solving the optimal scheduling model of the micro-grid by adopting a hybrid genetic particle swarm optimization algorithm MOPSO, and outputting an optimization result meeting a scheduling strategy. According to the method, the operation cost is optimized, the microgrid dispatching strategy with the optimal economical efficiency can be formulated by accurately predicting the future load and the new energy output and fully utilizing adjustable and controllable means such as energy storage and the hybrid genetic particle swarm optimization algorithm, and the operation cost is reduced.

The invention relates to a pollutant concentration diffusion simulation method based on grid optimization and self-regulation. The method comprises the following steps: carrying out basic grid division on a simulation area to obtain a basic grid; calculating a pollutant concentration field of the simulation area according to the spatial position and the pollutant concentration value of the monitoring station; generating a concentration distribution matrix according to the pollutant concentration field of the simulation area, and then dynamically adjusting the grid density of the basic grid; calculating the calculation complexity of each grid region by using the grid density of the adjusted basic grid, carrying out pollutant concentration calculation task allocation according to a cloud platform and a weighted polling algorithm, and forming a pollutant concentration distribution prediction result of the simulation region by a pollutant concentration prediction result of each grid; and dynamically adjusting a pollutant concentration distribution prediction result according to the Kalman filtering and the obtained real-time environment data to obtain a final pollutant concentration prediction value. By adopting the method, the precision and the real-time performance of pollution diffusion simulation can be improved.

The invention relates to the technical field of power supply systems, in particular to a source-grid-load-storage-based microgrid optimization control related device and method, and the method comprises the steps: obtaining photovoltaic power generation power, wind power generation power and load power utilization power at each moment in each collection period before each collection moment; constructing a target function; determining a power supply change rate and a power utilization change rate of each acquisition period; determining a power supply fluctuation index and a power utilization fluctuation index of each acquisition period; determining a supply balance coefficient of each acquisition period; determining a mutation coefficient of each collection moment; determining the inertia weight of the particle swarm optimization algorithm at each acquisition moment; and in combination with a particle swarm optimization algorithm, determining the charging and discharging power of the energy storage side in the collection period of each collection moment, and controlling the charging and discharging power of the energy storage side in the micro-grid. According to the method, the objective function can be accurately optimized, so that the charging and discharging power of the energy storage side can be accurately regulated and controlled, and the optimization control accuracy of the micro-grid is improved.

The invention provides a coupling micro-grid optimization scheduling method and system considering electric and thermal load demand collaboration, and relates to the technical field of micro-grid optimization scheduling, and the method comprises the steps: building a hydrogen-containing micro-grid operation model, considering the peak-valley complementary characteristics of an original electric load and a thermal load in the system in time distribution, building an electric and thermal load demand collaboration response model, and carrying out the optimization scheduling of the hydrogen-containing micro-grid operation model; the electric load is described by a time-of-use electricity price demand response method, and flexible adjustment is carried out based on different electricity prices by considering the heat transfer inertia of the heat load and the fuzziness of user temperature perception; based on a hydrogen-containing micro-grid operation model and an electric heating load demand collaborative response model, an objective function is constructed by taking the minimum sum of the total operation cost, the renewable energy wind and light abandoning cost and the demand response compensation cost as an objective, and an optimal dispatching model of the electricity-hydrogen coupling micro-grid is established according to constraint conditions. And solving the hydrogen-electricity coupling micro-grid optimization scheduling model by adopting mixed integer linear programming, and applying the solved equipment optimal operation scheduling strategy to micro-grid execution.

The invention discloses an oil and gas reservoir shale content logging curve intelligent prediction method, and belongs to the technical field of rock physics and logging curve processing and interpretation, and the method comprises the steps: in step 101, determining Gardner formula parameters through combining a determination coefficient and a grid optimization method, and determining a Gardner formula parameter; and then generating a virtual density data set capable of covering the actually measured density data set by using a Gardner formula. According to the method, the optimal parameters of the Gardner rock physical formula and the Larionov rock physical formula are determined by using the decision coefficient and the grid optimization method, so that the abnormal value of the actually measured logging data can be effectively corrected, and a fusion data set for training various machine learning models can be generated; a cross validation method, a correlation coefficient, a determination coefficient and a multi-dimensional SHAP method are comprehensively utilized to more comprehensively evaluate and explain a prediction result, compared with a traditional rock physics method, the shale content prediction precision is improved by 12%, a logging curve data set can be effectively corrected and expanded, and the prediction accuracy of the shale content is improved by 12%. And the accuracy and the reliability of predicting the shale content of the oil and gas reservoir can be obviously improved.

The invention discloses a method and a system for managing a source-load storage micro-grid containing cluster electric vehicles for the worst scene, and relates to the field of power system optimization scheduling, and the method comprises the following steps: S1, in the source-load storage micro-grid containing the cluster electric vehicles, carrying out optimization scheduling on the source-load storage micro-grid containing the cluster electric vehicles; establishing an operation constraint and coordination control model comprising an electric vehicle cluster, an energy storage unit, a gas turbine unit and a photovoltaic unit; s2, a two-stage three-layer robust optimization model of a min-max-min structure is established, decision variables are divided into a first-stage variable x and a second-stage variable y, and the two-stage three-layer robust optimization model takes the minimum daily operation cost of the microgrid as an optimization target and takes the operation constraints and the coordination control model in the step S1 as constraints; and S3, adopting a column and constraint generation algorithm to decompose the original problem in the step S2 into a main problem and a sub-problem for iterative solution. According to the invention, Camp is adopted; solving the established model according to a G algorithm idea to obtain an optimal day-ahead economic dispatching scheme of the micro-grid in the worst scene; the conservative property of the micro-grid optimization scheme can be flexibly adjusted by changing the uncertainty robust adjustment coefficient, and reasonable selection between the operation cost and the operation risk by a micro-grid dispatcher is facilitated.

The invention relates to a water-light storage area power grid optimization method based on federated and layered multi-agent reinforcement learning. The method comprises the steps of constructing a federated and layered multi-agent model, defining states, actions and reward spaces of layered agents, implementing federated reinforcement learning offline training with privacy enhancement, deploying the model and executing online distributed optimization. Through the federal learning framework, it is ensured that private data of all the energy main bodies are locally calculated, the privacy leakage risk caused by data collection in a traditional centralized method is avoided, and a technical basis is provided for building collaborative trust among multiple main bodies. Meanwhile, the designed hierarchical intelligent agent architecture decouples a complex power grid system into subproblems with clear hierarchies, and when the scale of the system is expanded, new energy units can be accessed in a modular mode, so that the overall maintenance and upgrading difficulty of the system is reduced, and the expandability of the system is improved.

The invention discloses a full-automatic, rapid and high-precision three-dimensional reconstruction method and system, and relates to the field of three-dimensional dynamic reconstruction. According to the method, the spectrum-point cloud fusion model is constructed, the Monte Carlo ray tracing is introduced to calculate the dynamic reflectivity field, and the Fresnel formula and the dynamic ray tracing technology are combined, so that the reflectivity calculation is more in line with the physical law, and the accuracy of spectrum mapping is improved. The spectral data is optimized and adjusted based on an error correction matrix through a spectral error compensation network, so that error accumulation caused by factors such as a measurement angle and surface roughness is reduced; besides, in the three-dimensional reconstruction process, the continuity and spectrum consistency of the three-dimensional model are improved through a Poisson surface reconstruction and spectrum reflectivity driven grid optimization method according to spectrum enhanced point cloud data.

The invention relates to the technical field of image processing, and discloses an intelligent spraying construction control method and system based on image processing and a medium. The method comprises the following steps: performing image acquisition and defect analysis on an outer wall base layer to generate reference data; performing lattice optimization and path planning based on the reference data; environment parameters are collected, a compensation database is established, and a process regulation matrix is generated; coating quality is monitored in real time, and a data set and an early warning library are generated; the spraying track is optimized, and a control instruction is generated; and evaluating the construction quality to form an evaluation system. Intelligent spraying quality real-time monitoring and self-adaptive adjustment based on image processing are realized, so that the automation level and the quality control precision of outer wall spraying construction are improved.

The invention discloses a micro-grid group low-carbon optimization operation method based on an improved MAPPO algorithm, and the method comprises the steps: building a micro-grid group optimization operation model with the minimum total cost in a single micro-grid operation period as an objective function; constructing a partially observable Markov decision process of micro-grid optimization operation, and establishing an observation space, an action space and a reward function corresponding to each micro-grid agent; non-linear constraints of carbon treatment cost and collaborative rewards among micro-grids are introduced into a reward function, and a negative sampling experience sharing framework is introduced into an MAPPO algorithm; the LSTM network is embedded into the MAPPO algorithm, and a differential learning rate attenuation strategy is designed to further improve the training speed of the LSTM-MAPPO model; and training the intelligent agent based on an improved MAPPO algorithm to obtain an optimal microgrid group low-carbon optimization operation scheme. The optimization method provided by the invention can effectively reduce the operation cost of each micro-grid and reduce the carbon emission of the system at the same time.

The invention discloses a laser point cloud-based industrial equipment digital twin model rapid reconstruction method and system, and the method combines a model matching mode and a point cloud reconstruction mode, and effectively solves the problem of twin model reconstruction in different scenes. According to the first mode, on the basis of an existing three-dimensional model, through shape matching, feature extraction and model retrieval methods, registration of point cloud data and the three-dimensional model is combined, and rapid reconstruction of the equipment twinborn model is successfully achieved. The mode is suitable for equipment with an existing standard three-dimensional model, the repeated utilization rate of the model can be improved, and the reconstruction time and cost are reduced. In the second mode, the reverse grid model construction method based on the point cloud data is provided for equipment which cannot be retrieved through an existing model. Through an improved greedy projection triangulation algorithm, point cloud data is converted into a grid model, and the precision and authenticity of the model are improved in combination with a grid optimization algorithm and a surface texture mapping technology. According to the method, the digital twinborn model of the industrial equipment can be efficiently and accurately reconstructed, the modeling difficulty in an industrial scene is relieved, the actual demand of field modeling is met, and the method has high application value and practicability.

The invention discloses an industrial park micro-grid optimization scheduling method based on electric furnace load active adjustment. The method comprises the following steps: constructing an economical operation objective function for generating power by using a thermal power generating unit to share blast furnace gas of a steel mill; dynamically adjusting the maximum exchange power between the micro-grid and the public grid by iterating the shrinkage constraint, and calculating a minimum feasible value to reduce basic electricity price expenditure; taking the gear of the electric furnace transformer as an adjusting variable, and constructing a load adjusting constraint model containing a fixed load and an adjustable load; the micro-grid adopts a double-bus topology, power mutual assistance is realized through a back-to-back converter, and a distributed power supply, an energy storage system and a new energy power generation set are configured. The method can efficiently utilize energy and reduce the operation cost; the public network expenditure is reduced by dynamically optimizing power exchange; the system flexibility is improved by flexibly regulating and controlling the load; new energy fluctuation is stabilized, and stability is enhanced; the double-bus structure realizes grid-connected and off-grid switching, improves reliability, and is suitable for iron and steel enterprises to improve the utilization rate of renewable energy sources and reduce operation cost.

The invention discloses a micro-grid optimal scheduling method based on a self-adaptive scheduling participation mutation particle swarm algorithm, and aims to improve the operation efficiency and environmental protection performance of a micro-grid. According to the method, a self-adaptive parameter adjustment method and a variation mechanism are innovatively provided, the problems that a traditional particle swarm algorithm is prone to premature and lacks population diversity are solved, and the global search capability and the optimization effect are remarkably enhanced. Constructing a mathematical model in a microgrid grid-connected operation mode by taking the operation cost and the environmental protection cost as optimization targets; scheduling optimization is carried out by using a self-adaptive scheduling participation mutation particle swarm algorithm; through comparison simulation experiments, the superiority of the method under different load requirements and resource distribution conditions is verified. The method is suitable for micro-grid optimization scheduling scenes including distributed power supplies, energy storage systems and the like, and maximization of economic and environmental protection benefits can be realized while power supply reliability is ensured.

The invention discloses a multi-energy micro-grid optimization scheduling method considering an electricity-hydrogen coupling flexibility margin. The method comprises the following steps: constructing each equipment model of a multi-energy micro-grid; a flexibility margin evaluation model is established based on the flexible adjustment characteristic of electro-hydrogen coupling, and flexibility margins and flexible adjustment capabilities of different devices and different coupling nodes are analyzed; a multi-energy micro-grid day-ahead optimization scheduling model considering electricity-hydrogen coupling is established, maximum system life cycle total profit and adjustment flexibility are taken as objective functions, and under constraint conditions of stable operation of photovoltaic wind power and electric hydrogen production equipment, capacity of hydrogen storage equipment, micro-grid system flexibility and the like, the objective functions are solved by adopting an improved particle swarm optimization algorithm, so that the optimal multi-energy micro-grid day-ahead optimization scheduling model is established. And obtaining a day-ahead optimization scheduling result. Meanwhile, the characteristic that the flexibility demand is uncertain due to the fluctuation of renewable energy sources and loads is considered, historical data are processed by using a Monte Carlo method, and the flexibility demand of the microgrid is quantitatively analyzed.

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.

The invention discloses an icing forecast condition gridding optimization method and system, and relates to the technical field of power system freezing disaster prediction, and the method comprises the steps: obtaining an icing terminal of a target region and the monitoring data of the icing terminal; acquiring historical meteorological elements, and interpolating the historical meteorological elements to the icing terminal to obtain a first data set; establishing a physical icing growth model based on the first data set; changing the icing condition of the physical icing growth model, and determining the optimal solution of the icing forecast condition parameters; according to the method, the rmse of the icing forecast can be relatively accurately reduced, the TS score of the icing forecast is improved, a scientific support is provided for the icing forecast of the power transmission line, and the method has important scientific significance and application value.

The invention relates to a micro-grid multi-objective optimization scheduling method and device, and belongs to the technical field of micro-grid optimization scheduling, and the method comprises the following steps: determining the structure of a micro-grid system, and collecting the equipment parameters of each distributed power supply; establishing a micro-grid optimization scheduling model according to the characteristics of the micro-grid and the collected equipment parameters, wherein the micro-grid optimization scheduling model takes minimization of user operation cost and environment cost as an objective function; generating an initial population of the MOPSO algorithm by using a chaotic equation, and improving the value of a learning factor by using a dynamic adjustment parameter to obtain an improved MOPSO algorithm; and performing optimization solution on the micro-grid optimization scheduling model by using an improved MOPSO algorithm to obtain the output power of each distributed power supply. According to the invention, the cost and environmental factors of the micro-grid are considered at the same time, and effective scheduling and management of the micro-grid are realized.

The invention discloses a micro-grid optimization scheduling method based on a distributed bee foraging algorithm. The method comprises the following steps: establishing a micro-grid optimization scheduling objective function; determining micro-grid optimization scheduling related constraint conditions; performing initialization setting on parameters of the distributed bee foraging algorithm; according to the optimization scheduling related constraint conditions, randomly generating an optimization scheduling scheme; evaluating and sorting nectar sources searched by the investigation bees; carrying out local search by the bees and updating the selected nectar source; carrying out local search by worker bees and updating the selected nectar source; the reconnaissance bees generate optional optimal scheduling schemes in a random search mode; the nectar sources searched by the investigation bees and the selected nectar sources are sorted together, and the selected nectar sources used for next iteration are screened out; and outputting a globally optimal solution and a fitness function value of the target function. According to the method, the bee foraging algorithm is improved to adapt to the optimization requirements of the distributed micro-grid, and the efficient solution of the distributed micro-grid optimization scheduling scheme is realized.

The invention relates to a power distribution network frame optimization method based on a quantum annealing algorithm. The method comprises the following steps: constructing an objective function of power distribution network frame optimization based on actual power distribution network data and setting constraints; obtaining a quantum Hamiltonian function based on the objective function and the constraint; based on the quantum Hamiltonian function, obtaining a secondary unconstrained binary optimization objective function of power distribution network frame optimization; based on the quantum Hamiltonian function or the quadratic unconstrained binary optimization objective function, solving a power distribution network frame optimization optimal solution; and generating a power distribution network frame optimization strategy based on the optimal solution. Compared with the prior art, the method has the advantages of higher calculation efficiency and precision and the like.

The invention relates to the technical field of power system real-time control, and discloses a multi-source distributed resource collaborative real-time control method, system and device and a medium. The method comprises the following steps: constructing a multi-target scheduling model fusing user comfort and power grid operation indexes; constructing an adaptive hybrid intelligent algorithm based on a non-dominated sorting genetic algorithm, a deep reinforcement learning algorithm and a particle swarm optimization algorithm; based on the real-time data, performing adaptive solution on the multi-target scheduling model according to an adaptive hybrid intelligent algorithm to obtain a minute-level optimization decision scheme; and issuing a control command according to the minute-level optimization decision scheme so as to realize deep cooperative control of the multi-source distributed resources. According to the method, the user comfort is used as a constraint to be fused into a power grid optimization target, and deep cooperative control of multi-source distributed resources, namely photovoltaic-energy storage-load, is realized through autonomous learning and optimization of an adaptive hybrid intelligent algorithm, so that the power grid peak clipping and voltage regulation capabilities are effectively enhanced while the user satisfaction is improved.

The invention belongs to the technical field of energy management, and provides an integrated energy micro-grid energy management and transaction method under user demand response, which comprises the following steps of: 1, establishing an integrated energy micro-grid energy equipment model, a schedulable model of flexible loads such as an electric vehicle and a variable frequency air conditioner, and an operation optimization model of the schedulable model participating in market transaction; step 2, constructing a neural network required by MATD3; step 3, according to the model established in the step 1, utilizing an energy management center to interact with an interconnected micro-grid simulation environment, putting historical information obtained by interaction into an experience pool, and utilizing TD3 to realize optimization of a strategy network and an evaluation network; and step 4, controlling the micro-grid according to the optimized strategy network obtained in the step 3 so that the micro-grid can participate in the end-to-end market transaction. According to the method, nearby consumption of distributed energy is facilitated, redundant capacity and renewable energy of the comprehensive energy micro-grid are further utilized, and the economical efficiency of a micro-grid optimization scheduling strategy is taken into account.

The invention provides a micro-grid optimization scheduling method, system and device and a readable storage medium, and the method comprises the steps: constructing a micro-grid system which is used for collecting the operation data of a distributed power generation device and energy storage equipment in real time, and adjusting the load of a user side through executing a scheduling strategy; constructing a micro-grid optimization scheduling model, setting an objective function of the model, setting constraint conditions of the model, and calculating an optimal operation strategy of the micro-grid system by solving the micro-grid optimization scheduling model; constructing a QUBO model, and associating the QUBO model with the micro-grid optimization scheduling model to determine an optimal operation strategy of the micro-grid system by calculating an optimal solution of the QUBO model; based on the QUBO model, performing conversion processing on the objective function and the constraint condition of the micro-grid optimization scheduling model to determine the objective function of the QUBO model; and obtaining operation data of the micro-grid system in real time, and generating an optimal operation strategy through the micro-grid optimization scheduling model.

The invention discloses a micro-grid operation strategy optimization method based on deep reinforcement learning, electronic equipment and a storage medium, and relates to the field of micro-grids, and the method comprises the steps: 1, building a micro-grid optimization model taking a power generation unit, a storage unit and tie line interaction power as control objects, the method comprises the steps of (1) determining a micro-grid optimization model, (2) determining constraint conditions of the micro-grid optimization model, (3) randomly sampling and generating an initial sampling point set, (4) constructing a PPO-clip algorithm model based on the initial sampling point set, and (5) estimating and solving an optimal solution of the micro-grid optimization model by using the PPO-clip algorithm model and optimizing micro-grid operation. According to the method, the PPO-clip algorithm is adopted to assist the establishment of the micro-grid optimization model, the optimization of the micro-grid optimization model can be realized in a relatively low round, so that the micro-grid optimization model realizes efficient power generation, storage and electric quantity adjustment of the wave crest and the wave trough, and an optimal solution of data is realized.

The invention discloses a multi-target collaborative optimization scheduling control method and system for an optical storage and charging micro-grid, and aims to solve the multi-target conflict problem of economy, environmental protection and stability in the operation of the micro-grid by constructing a micro-grid model containing photovoltaic, energy storage, load and a power grid. A multi-objective function set for economic cost minimization, carbon emission minimization and supply and demand balance optimization is constructed, and an adaptive multi-population genetic algorithm and a Pareto optimization algorithm are innovatively proposed to collaboratively iteratively solve a Pareto optimal solution set. The search efficiency is improved through an adaptive variation rate mechanism and a multi-population strategy, and the problem of multi-target conflict is effectively solved. The method is suitable for optimal scheduling of the independent micro-grid containing photovoltaic power generation, an energy storage system and a charging load, and the energy utilization efficiency and the system stability are improved.

The invention discloses a micro-grid scheduling strategy generation method and device, equipment, a medium and a product, and belongs to the technical field of micro-grid scheduling. The method comprises the following steps: constructing a micro-grid optimization scheduling physical model, wherein the micro-grid optimization scheduling physical model at least comprises a flexible load response model; based on the flexible load response model, establishing a multi-objective optimization function including a power supply reliability objective and an operation cost objective; system operation constraint conditions of the multi-objective optimization function are set, and the system operation constraint conditions at least comprise distributed power supply output constraints; and carrying out optimization solution on the multi-objective optimization function by adopting an optimization algorithm to obtain a micro-grid scheduling strategy, and carrying out cooperative control on various resources in the micro-grid based on the micro-grid scheduling strategy. According to the micro-grid scheduling strategy generated by the embodiment of the invention, the efficiency and robustness of micro-grid scheduling can be effectively improved.

The invention belongs to the technical field of energy management and power system optimization, and particularly discloses a port microgrid optimization scheduling method, device and equipment, a storage medium and a product. According to the application, the microgrid operation optimization model is constructed according to the predicted load demand, the predicted wind power generation power and the predicted solar power generation power; solving a target scheduling strategy according to the power grid operation optimization model and the multi-dimensional microgrid operation constraint conditions; and performing optimal scheduling on the port microgrid according to the target scheduling strategy. Through the above mode, comprehensive optimization of microgrid operation is realized by using the microgrid operation optimization model on the basis of considering the operation cost and the environment cost, the operation stability is ensured according to the multi-dimensional microgrid operation constraint condition, and then optimization scheduling is performed on the port microgrid according to the target scheduling strategy. Therefore, the accuracy of dispatching the port micro-grid can be effectively improved, the operation economy and the energy utilization efficiency of the micro-grid are improved, and carbon emission is reduced.

The invention provides a wind and light storage micro-grid optimization scheduling method and system containing small hydropower storage, and the method comprises the steps: obtaining historical operation data, and carrying out the prediction according to the historical operation data, and obtaining a full-dimension prediction data set; according to the full-dimension prediction data set, constructing a multi-objective optimization model with the goal of minimizing residual load, minimizing power abandoning cost, minimizing cascade water abandoning amount, maximizing green energy collaborative utilization efficiency and minimizing time weighted voltage-power comprehensive error; and solving the multi-objective optimization model by adopting a hybrid optimization algorithm fused by a Hanniac optimization algorithm and an improved sparrow search algorithm to obtain a final output. According to the invention, efficient consumption of green energy can be realized, the electricity abandoning phenomenon is reduced, carbon emission is reduced, and the stability, economical efficiency and environmental protection property of a power system are improved.

The invention provides a scene-based random optimization method for micro-grid energy and flexibility scheduling, and belongs to the technical field of micro-grid optimization scheduling. The method comprises the following specific steps: minimizing the expected cost of the micro-grid as a target; setting a power balance constraint in the micro-grid according to a relation among photovoltaic power generation capacity, power load power consumption, battery energy storage charging and discharging power and power loss in the micro-grid internal transmission and conversion process in the micro-grid; determining a flexible service constraint of the micro-grid according to the flexible service model and the battery energy storage model; predicting and updating by using a rolling time domain method, modeling uncertainty of the micro-grid by combining scene generation and scene reduction technologies, determining a day-ahead prediction error of the micro-grid, and constructing a random optimization model of energy and flexibility of the micro-grid; based on a flexible intra-day framework, solving a random optimization model of micro-grid energy and flexibility according to a scene at different time stages, and obtaining a flexible scheduling strategy; meanwhile, the stability and efficiency of the micro-grid are considered.

The invention discloses a free grid layout conversion system and method based on a WEB end and a storage medium, and relates to the technical field of grid layout optimizing.The free grid layout conversion system comprises a user interface module, a layout analysis module, a grid self-adaption module, a response type design module, a grid optimization module and a data storage module, the modules are in electric signal connection; and the user interface module is used for providing an interactive interface and supporting real-time preview, so that a user can check the layout adjustment effect in real time. According to the method, the grid size and layout are dynamically adjusted, so that the flexibility and adaptability of the webpage layout are improved, the grid layout adaptive to different equipment and screen sizes can be automatically generated according to the input parameters and rules of the user, the webpage content can be ensured to keep good readability and layout effect in various environments, and the user experience is improved. The user experience is further improved, and the workload of customizing layouts for different devices is reduced.