12 results about "Smart microgrid" patented technology

This invention discloses a multi-source collaborative optimization control method for smart microgrids, relating to the field of energy technology. The method includes: connecting to the user side to predict the power supply demand of the target microgrid in a preset time zone; acquiring a hybrid energy supply module containing multiple distributed power sources; collecting historical power supply logs of the distributed power sources and generating relevant time sequences; performing energy curtailment rate analysis based on this to generate indicators and decisions; optimizing energy storage units after determining that the indicators meet the standards; and then performing multi-source collaborative power supply control according to preset demand. This method solves the technical problems of existing new energy power generation uncertainties that are difficult to accurately predict, and unreasonable energy storage module configurations that lead to significant energy losses during power generation, storage, and distribution, resulting in low energy utilization efficiency and unstable power supply quality. By optimizing and integrating new energy power generation, energy storage modules, and energy distribution in the microgrid, it achieves the technical effect of reducing energy losses and ensuring efficient and high-quality power supply to the microgrid.

This invention relates to the fields of new energy and smart grid technology, and discloses a single-point grid-connected distributed photovoltaic system based on energy storage and a smart microgrid. The system includes: a distributed photovoltaic cluster, an electrochemical energy storage system, a smart microgrid management system, and a single public grid connection point. By constructing a "single-point grid connection, unified control" architecture, this invention integrates the originally dispersed, multi-point grid-connected distributed photovoltaic clusters into a single entity. Through a unified smart microgrid management system for coordinated control, it achieves centralized management and unified scheduling of large-scale distributed power sources, solving the problems of "difficult grid scheduling and 'invisibility and uncontrollability' caused by dispersed multi-point grid connection." It transforms disordered access into orderly access, greatly simplifying the complexity of grid scheduling and management, and improving the grid's capacity to accept high-penetration distributed photovoltaic power and its operational safety.

This invention discloses an intelligent microgrid energy management system based on wind, solar, and energy storage integration, relating to the field of microgrid energy management technology. It includes an energy management center, which is communicatively connected to the following modules: a collaborative current stabilization module, used to construct a distributed collaborative control network with energy storage modules as intelligent agents. This invention achieves rapid voltage self-stabilization under decentralized scheduling by constructing a distributed collaborative control network with energy storage modules as intelligent agents, combined with consensus algorithms and digital twin simulation. Each energy storage module intelligent agent calculates adjustment commands in real time based on its local state and responds collaboratively with adjacent nodes, effectively suppressing DC bus voltage oscillations. Even in the event of communication delays or local equipment failures, voltage stability can still be maintained through distributed collaboration, significantly enhancing dynamic robustness and operational continuity under both off-grid and grid-connected conditions.

This invention discloses a dynamic energy storage regulation system for smart microgrids applicable to new energy power plants, relating to the field of power grid management technology. The invention acquires monitoring information of disturbance events occurring in the target system through a condition monitoring module. A prefetching management module prefetches several monitoring values ​​of each acquisition parameter into a cache according to the prefetching strategy for each acquisition parameter. A data analysis module analyzes the retrieval status of all monitoring values ​​prefetched into the cache for each acquisition parameter during various disturbance events, determining the comprehensive impact of each acquisition parameter under the disturbance event. The prefetching management module adjusts the prefetching frequency of each acquisition parameter's monitoring values ​​accordingly when the next disturbance event occurs. In this way, dynamic adaptation of the prefetching of each acquisition parameter during disturbance events can be achieved, solving the problems of resource waste and insufficient prefetching caused by fixed-frequency prefetching in traditional prefetching methods.

This invention relates to a distributed energy collaborative optimization method based on a cloud-edge-device architecture, belonging to the field of smart microgrid technology. Technical solution: S1. Define a four-layer cloud-edge-device architecture; S2. Information transmission stage: Information is reported upwards and circulated and transmitted at the same level; S3. Demand optimization stage: Demand is decomposed downwards and optimized collaboratively at the same level; S4. Energy flow stage: Energy is aggregated upwards and interacts and communicates at the same level. This invention, through its designed four-layer cloud-edge-device architecture, enables distributed energy to shift from centralized control to active participation in demand response. Based on this, a distributed energy collaborative optimization method based on the cloud-edge-device architecture is further designed, achieving full-domain collaborative optimization of distributed energy. Firstly, it reduces the response error of distributed energy; secondly, it effectively reduces the response time of distributed energy, solving the problem of excessive computing power in traditional centralized control; and thirdly, it reduces the operating cost of distributed energy, addressing the high operating cost of distributed energy.

This invention relates to the field of smart microgrid energy management technology and discloses an integrated smart footwear storage location management system, including a distributed resource storage module, a load flux coupling monitoring module, a dynamic virtual impedance generation module, and a load balancing scheduling control module. By monitoring the load flux density of logical partitions in real time to construct a nonlinear dynamic virtual impedance, and combining it with the correlation coupling degree model to calculate the comprehensive acceptance potential energy, load scheduling instructions are generated. This invention utilizes an embedded negative feedback saturation suppression loop to exponentially increase the impedance when the flux density approaches the threshold, forcibly suppressing the load adsorption capacity of high-heat nodes, and realizing dynamic power balance between energy flow and information flow within the system.

The application provides a solid-state transformer and a load-side self-current-sharing control method and system thereof, and relates to the field of power electronic converters.The method steps are as follows: step S1 signal acquisition, step S2 average value calculation, step S3 voltage outer loop control, step S4 current inner loop control, step S5 each module feedforward compensation and control quantity generation, and step S6 dynamic self-current-sharing adjustment.Through a three-layer composite control architecture of "voltage outer loop for output voltage, current inner loop for total current, and DC voltage feedforward for current sharing", the DC voltage inside each module is used as a current sharing proxy variable, and under the premise of no high-speed communication between modules, high-precision, fast-response, and strong-robust self-current-sharing control of the solid-state transformer output side multi-module parallel connection is realized, the system reliability is significantly improved, the hardware cost is reduced, and the method is widely applicable to key scenes such as AI data center DC power supply, intelligent microgrid, and new energy grid connection.

The present application relates to the technical field of energy internet and intelligent microgrid, and discloses a carbon flow probability prediction and uncertainty quantification method for wind-solar-hydrogen storage microgrid, which first acquires microgrid multi-source operation data and constructs an input feature vector; a carbon flow quantile regression neural network model is established, and a quantile loss function is used to fit the conditional distribution of carbon flow; an adversarial perturbation mechanism is introduced in the training stage, and adversarial samples are generated along the gradient rising direction of the loss function to construct a mixed total loss function to update the parameters and promote the model to learn the response boundary of the input data under the adversarial perturbation; finally, the trained model is used to output high and low quantile point prediction values, a carbon flow probability prediction interval is constructed, and the interval is transmitted to the microgrid energy management system. Through the combination of quantile regression and adversarial training, the present application realizes the explicit quantification of carbon flow uncertainty, improves the robustness of the prediction model under extreme working conditions, and provides a reliable basis for low-carbon risk decision-making of the microgrid.

This invention discloses a cooperative control system and method for intelligent microgrids based on reinforcement learning, comprising: collecting cooperative operation data of the microgrid and preprocessing it to output a standardized operation dataset; constructing state sequence samples to generate a training state sequence set; pairing these samples with control actions and subsequent states to form an expert trajectory library; constructing an improved Reformer model to obtain a multi-agent consistent control representation; generating an access frequency distribution and merging it into an occupancy sample set; obtaining a reward function based on maximum entropy inverse reinforcement learning; and completing policy training to form an online control strategy. This invention, by combining an improved Reformer model and maximum entropy inverse reinforcement learning, achieves real-time and stable cooperative control that satisfies voltage frequency and energy storage state of charge constraints.

This invention discloses a method for configuring and dynamically scheduling a photovoltaic-storage-charging system in an industrial park that integrates smart power distribution, belonging to the fields of new energy microgrids and smart power distribution technology. The method includes: constructing an integrated photovoltaic-storage-charging system capable of both grid-connected and islanded operation; selecting key equipment for photovoltaics, energy storage, and charging piles and modeling their full lifecycle costs; constructing a collaborative optimization configuration model with economic and operational constraints, aiming at optimizing the total system cost, and solving it using a mixed integer programming algorithm; establishing a comprehensive power quality evaluation system from the dimensions of flexibility, power supply quality, new energy absorption rate, and carbon emissions, and using a combined weighting method to achieve quantitative grading of system performance; and developing an interactive visualization design platform based on MATLAB / GUIDE, supporting multi-source data import, parameter adjustment, and multi-scheme comparison. This invention achieves synergistic optimization of the economy and energy efficiency of the photovoltaic-storage-charging system, lowers the planning threshold, and is applicable to the low-carbon transformation of industrial parks and smart microgrid planning.

This invention relates to the field of smart microgrid technology, specifically disclosing a modular photovoltaic carport microgrid system based on AI-driven photovoltaic-storage synergy. The system includes: a hardware construction module, a load forecasting module, a photovoltaic power generation forecasting module, and a global optimization module. The system constructs a modular photovoltaic carport, collects multi-source operating data using a deployed sensor network, sets a load forecasting cycle, builds a historical charging database and performs periodic statistical forecasts, generates a charging probability distribution for each track section within the load forecasting cycle, determines whether to trigger mobile charging terminal location adjustments, divides the control cycle based on the load forecasting cycle, constructs and uses a photovoltaic time-series forecasting model for prediction, generates a photovoltaic power generation forecast curve within the control cycle, and obtains a global prediction data package within the control cycle by combining the charging probability distribution. Based on the global prediction data package, a multi-objective optimization function is constructed and solved, and the corresponding optimal control instruction set is output.

The utility model provides a current signal acquisition circuit for intelligent microgrid, including reference feedback circuit, signal sampling circuit and feedback proportional operation circuit, reference feedback circuit includes first operational amplifier, and the output of first operational amplifier connects secondary current feedback signal, and the noninverting input of first operational amplifier connects preset reference voltage signal, and the inverting input of first operational amplifier connects signal sampling circuit, signal sampling circuit includes second operational amplifier, and second operational amplifier connects primary current feedback signal, and second operational amplifier connects reference feedback circuit, and feedback proportional operation circuit includes third operational amplifier, and third operational amplifier receives secondary current feedback signal, and third operational amplifier connects signal sampling circuit, and the output of third operational amplifier exports current conversion signal, the utility model discloses the current conversion signal of output is more accurate.