369 results about "Micro grid" patented technology

The application relates to a sequence-dependent plug-in island direct-current micro-grid distributed control method and system, first, local electrical parameters and interconnection line parameters of each DGU are acquired, an island direct-current micro-grid state space model is established, and a distributed controller model is constructed based on local state variables of the DGU; second, aiming at the access or cut-off request of the target DGU, the cut-off request is equivalently converted into the access request in the sequence-dependent plug-in scene, a to-be-judged insertion sequence or a candidate insertion sub-sequence is generated; finally, a linear matrix inequality constraint is constructed according to a Lyapunov asymptotic stability condition, controller parameters meeting the stability requirement are solved, and pre-stability judgment and parameter optimization of the plug-in operation are realized. The application can effectively guarantee the asymptotic stability of the island direct-current micro-grid in the dynamic plug-in scene, and improve the plug-and-play capability of the system.

The application relates to the technical field of micro-grid control, in particular to a photovoltaic storage and charging micro-grid control method and system for a weak power grid. The method comprises the following steps: acquiring a photovoltaic panel information set, analyzing the coupling influence of light reflection and internal load impact on the photovoltaic panel based on the photovoltaic panel information set, and obtaining a photovoltaic panel dynamic disturbance information set; acquiring a fish farm information set, analyzing the multi-objective coordination and trade-off relationship between fish farming demand and power generation demand based on the fish farm information set and in combination with the photovoltaic panel dynamic disturbance information set, and obtaining an optimized scheduling information set; generating a multi-end coordinated control strategy based on the optimized scheduling information set, and outputting a photovoltaic storage and charging micro-grid control log. The method makes the micro-grid load disturbance analysis more accurate and builds a data foundation for collaborative regulation of breeding and power generation.

The application provides a voltage imbalance treatment method and system for a micro-grid black start process under an unbalanced load, comprising the following steps: S1, determining that the focus point for treating voltage imbalance of a three-phase three-wire inverter is to eliminate negative sequence voltage; S2, introducing a mathematical model describing a two times power frequency oscillation disturbance signal characteristic generated by the negative sequence voltage into a control loop; and S3, using a proportional integral control method combined with a quasi-resonant controller to suppress the two times power frequency oscillation component in the control loop, so as to realize negative sequence voltage suppression and voltage imbalance treatment. The application analyzes the mechanism of voltage imbalance problems of the three-phase three-wire inverter under various unbalanced loads, and reveals that the focus point for treating voltage imbalance problems is to eliminate negative sequence voltage.

The application discloses a micro-grid digital sand table system based on real statistical law scene data generation, relates to the related field of micro-grid simulation and optimal control technology, and comprises an artificial configuration layer, which is used for receiving micro-grid static parameters configured by a user; a scene automatic generation layer, which is used for automatically generating dynamic meteorological, load and electricity price time sequence data for driving simulation based on historical statistical law; a physical simulation layer, which is used for calculating real-time power, energy state and operation cost of the micro-grid according to the generated data; an equal power mapping calibration module, which is used for collecting measured data of the physical world, comparing and correcting model parameters with the output of the physical simulation layer, so that the digital sand table satisfies the equal power characteristic; and a high-speed control interface, which is used for interacting with external control algorithms in terms of state information and control instructions. The problems of strong dependence on scene data, insufficient scene coverage, missing simulation fidelity and equal power characteristic and lack of large-scale training data support of the existing micro-grid simulation method are solved.

This invention relates to the field of microgrid control and discloses a method for regulating a highway service area microgrid based on distributed photovoltaic energy storage. The method includes the following steps: First, traffic flow data is collected to construct a traffic-energy potential field model, mapping discrete vehicles to a continuous potential field to predict net power demand. Second, a kinetic-thermal entropy mapping is constructed using vehicle kinetic energy characteristics, and battery thermal management feedforward control is executed before load arrival. Simultaneously, variational mode decomposition parameters are adaptively adjusted according to the potential field gradient to decompose the power command into high-frequency and mid-to-low-frequency components. The high-frequency components are absorbed by supercapacitors, while the mid-to-low-frequency components are collaboratively distributed among battery packs based on a virtual complex impedance model constructed using health status and temperature. This invention achieves proactive defense against impact loads and refined lifespan extension of energy storage through traffic-energy cross-domain coupling, significantly improving the voltage rigidity and operational stability of the microgrid under extreme conditions.

This invention discloses a coordinated control system and method for a megawatt-level photovoltaic-storage-charging microgrid, belonging to the field of integrated energy management and microgrid control technology. The system includes a grid interface, a photovoltaic power generation array, a large-scale energy storage system, a charging pile cluster, and a microgrid management system. The method constructs a five-layer efficient control logic system comprising a prediction layer, an energy control layer, a load control layer, an operation layer, and a value-added layer. Its core lies in using the power balance equation as a basis to dynamically adjust the charging and discharging power of the energy storage system, thereby maximizing the absorption of photovoltaic output, accurately managing grid demand, and adaptively adjusting the charging load. This invention introduces the concept of a photovoltaic discharge threshold that matches the physical start-up characteristics of photovoltaics. Based on the dynamic control logic of charging photovoltaics with power ramping, it achieves the operational goal of photovoltaic power utilization exceeding 70%, significantly reducing charging costs and improving the overall economy and reliability of the system.

The present application relates to a kind of microgrid optimization scheduling method and system combining double-layer game and A3C, comprising the following steps: constructing source-grid-load-storage integrated microgrid model;With the total income of maximum microgrid operation as the goal, combined with the operation constraints of microgrid, the constraints of different external environmental conditions factors on the on-site renewable energy power output, power constraint and microgrid power supply and demand balance constraint, establish the microgrid optimization scheduling model based on A3C;Extract state space feedback from environment, make pricing decision through upper Stackelberg game, as the leader of large power grid, the decision of microgrid as follower obtains current power purchase and V2G usage, updates price signal;Solve the trained microgrid scheduling model, obtain the strategy output by lower reinforcement learning at current time step.The present application has the advantages of realizing dynamic price signal closed-loop linkage, suppressing early convergence through adaptive exploration, etc.

The application discloses a kind of wind light storage micro-grid optimization operation method and system.The method adopts three-layer architecture of top optimization, middle layer cooperation and bottom execution, and forms the intelligent closed loop of " prediction-planning-coordination-execution-feedback ".Top is based on LSTM prediction and traffic jam algorithm to carry out global power flow congestion identification and dredging, and utilizes improved particle swarm algorithm in feasible interval to carry out multi-objective fine optimization, and generates optimal operation instruction;Middle layer dynamically adjusts optimization target weight according to operation scene, and decomposes instruction into multiple device cooperation strategy;Bottom execution customized control algorithm, realize device accurate regulation and millisecond level fast protection.The application effectively solves the problems of micro-grid multi-objective cooperative optimization, prediction control disconnection and operation risk foresight protection, and significantly improves the voltage stability, economy, dynamic response speed and safety and reliability of system operation.

This disclosure provides a microgrid dynamic disturbance analysis system and method. The microgrid dynamic disturbance analysis system includes: a detection and monitoring unit configured to collect operational data of the microgrid; a data platform configured to: calculate the dynamic stability parameters of the microgrid system based on a microgrid mathematical model using the collected operational data, and predict the dynamic disturbance parameters of the microgrid system based on a neural network model; and visualize the dynamic stability parameters and dynamic disturbance parameters through a microgrid digital twin model and return microgrid control commands.

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.

A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.

This invention discloses a method and system for visualizing and optimizing energy flow in microgrids. The method includes: obtaining an oscillation index based on the original power characteristic data of microgrid monitoring points; selecting several first monitoring points when the oscillation index is less than a first preset threshold; and repeatedly performing detailed judgments on the monitoring points when the oscillation index fluctuation coefficient is greater than or equal to a second preset threshold, until the oscillation index fluctuation coefficient is less than the second preset threshold or the number of iterations reaches a preset upper limit; selecting several second monitoring points to obtain an oscillation risk cumulative deviation coefficient; and increasing the monitoring frequency of the smallest circuit loop to which the second monitoring point belongs to a preset frequency and issuing an early warning when the coefficient is greater than or equal to a third preset threshold. This invention can automatically switch power control modes, adjust monitoring frequencies, inject damping current, balance reactive power output, and dynamically optimize carbon flow management according to different early warning levels, realizing multi-level risk inspection and adaptive collaborative control throughout the entire microgrid grid connection process.

This disclosure provides a day-ahead and intraday collaborative robust optimization scheduling method for hydrogen-containing multi-energy microgrids, relating to the field of microgrid integrated optimization technology. The specific implementation involves: constructing a multi-stage robust optimization scheduling model incorporating uncertain variables and unexpected mechanisms; performing day-ahead offline training on the multi-stage robust optimization scheduling model to obtain a day-ahead stage unit combination scheduling strategy for handling worst-case scenarios and an intraday multi-stage robust optimization scheduling model; generating control commands based on the day-ahead stage unit combination scheduling strategy for handling worst-case scenarios to control the operating status of the unit combination components during the day-ahead stage; applying the intraday multi-stage robust optimization scheduling model online during the day to obtain energy flow scheduling strategies for each stage of the day; and generating control commands based on the energy flow scheduling strategies for each stage of the day to control the operating status of the energy flow components for each stage of the day. This disclosed technical solution can improve the feasibility and robustness of the intraday scheduling strategies for hydrogen-containing multi-energy microgrids.

This invention provides a method, device, system, and medium for power grid load frequency control based on large-scale multi-agent deep meta-reinforcement learning. It constructs a cooperative load frequency control (CC-LFC) framework by mimicking a squid distributed neural network, modeling each regional controller and power distributor as agents. A large-scale counterfactual multi-agent deep meta-policy gradient (LSCMA-DMPG) algorithm is proposed, integrating centralized training with distributed execution and meta-reinforcement learning to achieve multi-task collaborative learning. Validated using a microgrid model of the four districts of Sansha Island in the Southern Power Grid, this method outperforms existing deep reinforcement learning algorithms and traditional centralized strategies in reducing frequency deviation, suppressing tie-line power fluctuations, and lowering power generation costs, significantly improving the reliability and economy of multi-regional microgrid operation.

The invention discloses a dispatching optimization method and system for a mine micro-grid, and relates to the technical field of micro-grids. The method comprises the steps of constructing a system model of the mining area comprehensive energy system; a mining area comprehensive energy operation model is constructed based on the system model, and constraint functions of the mining area comprehensive energy operation model comprise a net income maximization function, a renewable energy consumption maximization function and a carbon emission minimization function; the model is converted into a Markov decision process for determining a scheduling strategy for performing day-ahead scheduling control on the mining area integrated energy system, and the scheduling strategy comprises an output power range and an equipment state of each equipment in a day-ahead scheduling period; and under the limitation of the agreed range of the scheduling strategy and the constraint condition of the mining area comprehensive energy operation model, with the purpose of minimizing the operation cost, performing intra-day scheduling control by adopting mixed integer linear programming. Therefore, the problem that a scheduling optimization method in the prior art cannot be applied to the mine micro-grid or has many defects during application is solved.

This invention belongs to the fields of AC / DC microgrid "interface converters", energy storage systems and new energy power generation, and active reactive power compensation, and particularly relates to a discrete indirect power control method and device for grid-connected inverters. The control method in this invention is implemented in the αβ stationary coordinate system, eliminating the need for synchronous rotational coordinate transformation calculations. In this invention, the reference value of the grid-connected inverter output current is obtained through calculations using active / reactive power reference values ​​and grid voltage. A current-type pulse train (PT) control method is used to make the output current follow its reference value, thereby achieving the indirect power control objective. Therefore, the control method provided by this invention does not require an error amplifier and its compensation network (proportional-integral controller), the controller parameter design and digital implementation are relatively simple, and it has the advantage of low software cost, possessing certain academic research and engineering application value.

This invention discloses a microgrid day-ahead scheduling optimization method based on an improved learning strategy using BWOA (Brainwave Oscillator), comprising: 1) determining the dimension of the microgrid power output matrix according to the types of power sources in the microgrid, and adding output constraints of each micro-power source in the microgrid as constraints for each dimension of the microgrid power output matrix; 2) initializing the population according to the constraints, and obtaining an optimized initial population based on a reverse learning strategy; 3) iterating the optimized initial population, and solving for the minimum energy consumption of the microgrid as the objective function. This invention improves BWOA using a reverse learning strategy and optimizes microgrid day-ahead scheduling using the improved BWOA, thereby reducing the energy consumption of microgrid day-ahead scheduling and improving the absorption rate of clean energy in the microgrid day-ahead scheduling strategy, thus ensuring the safe and stable operation of the microgrid power system and the development of new energy sources.

The application provides a kind of energy storage microgrid collaborative scheduling method based on photovoltaic prediction and related equipment, the method comprises: the fusion processing of photovoltaic power generation historical data and meteorological sensor data is obtained feature data set;Photovoltaic output prediction is carried out to the pre-trained photovoltaic output prediction model, and the photovoltaic output prediction result containing prediction value and confidence interval is obtained;According to photovoltaic output prediction result and confidence interval, the state of charge of energy storage, grid interaction margin and load demand are comprehensively evaluated, and dynamic scheduling decision parameter is obtained;According to dynamic scheduling decision parameter, the energy flow between photovoltaic array, energy storage system, local load and grid is collaboratively distributed, and scheduling execution scheme is obtained.The uncertainty information of photovoltaic output prediction is introduced into the scheduling decision process, and the collaborative balance and stable operation of microgrid energy flow are guaranteed in the photovoltaic output fluctuation scene.

The present application relates to the technical field of micro-grid, and provides a maintenance plan generation method and system suitable for high-load local isolated network, comprising: for each month in each maintenance plan date combination, calculating the power shortage when each thermal power unit is shut down; based on the power shortage, combining the new energy output in previous years, calculating the new energy supplement power when each thermal power unit is shut down in each month; based on the new energy supplement power, calculating the daily power purchase amount when each thermal power unit is shut down in each month; for each maintenance plan date combination, accumulating the daily power purchase amount of all days to obtain the grid power purchase amount corresponding to the maintenance plan date combination, and taking the maintenance plan date combination corresponding to the minimum grid power purchase amount as the annual optimal maintenance plan. By using the certain periodicity of new energy in time, the power gap in the power grid power shortage state is made up, the multi-energy complementary effect is realized in the maintenance plan aspect, and the external power purchase amount is reduced.

The application provides a kind of energy storage converter VSG mode energy management coordination control method and system, comprising: according to the power of energy storage converter and operation command, calculate the total power of energy storage converter, and according to the state of charge of the battery corresponding to energy storage converter, divide the state of charge interval where each battery is located;Based on total power and state of charge interval, calculate the state of charge deviation rate of each battery according to the state of charge of each battery;Based on total power and state of charge deviation rate, calculate the active power allocated to each state of charge interval battery corresponding to energy storage converter respectively;According to the active power allocated to each energy storage converter respectively, obtain the active power given value of each energy storage converter.The method and system carry out energy management coordination control to energy storage converter, maintain the stable and reasonable operation of micro-grid system without energy management system, prevent overdischarge and overcharge of battery at the same time, and protect the service life of battery.

The present application belongs to the technical field of micro-grid energy management and unmanned aerial vehicle intelligent inspection, and discloses a kind of unmanned aerial vehicle cluster topology dynamic switching control method for urban multi-obstacle environment. With link availability probability as the unified dimension, based on equivalent additional loss, fusion of shelter, multipath, LOS / NLOS and infrastructure availability, a discrete topology state space is constructed. A semi-Markov jump model is used to describe the residence time and transition law, to predict the jump probability and remaining residence time in a finite time domain. Under the guidance of prediction, candidate topologies, attitude fine-tuning / link activation strategies are generated, and minimum residence, hysteresis mechanism and switching penalty constraints are applied. The optimal topology is selected by distributed consistency optimization, and after execution, it is re-optimized by feedback rolling. This method reduces the risk of frequent topology switching and disconnection, and improves connectivity and end-to-end delay controllability.

The present application relates to the technical field of power system regulation, and more particularly to a coordinated regulation method and system for main-distribution-micro multi-level vehicle-grid interaction, and a medium, the method comprising: constructing an electric vehicle aggregation model corresponding to a day-ahead time scale and an intra-day time scale according to historical operation data, real-time access state and state of charge of multiple types of electric vehicles; constructing a main-distribution-micro multi-level electric vehicle aggregation body coordinated regulation model based on the electric vehicle aggregation model and the hierarchical relationship between the main grid, the distribution grid, the micro grid and the charging station; generating coordinated regulation instructions for each level based on the coordinated regulation model, and distributing charging and discharging power to electric vehicles connected in the charging station to achieve orderly charging and discharging regulation. Through the present application, the problem that the prior art cannot perform multi-time scale coordinated aggregation and real-time orderly regulation of multiple types of electric vehicles in the main-distribution-micro multi-level vehicle-grid interaction scenario is effectively solved.

This invention relates to the field of microgrid control technology, specifically to a multi-timescale hierarchical collaborative system and method for microgrids. The system includes: a day-ahead optimization scheduling layer for formulating the next day's power generation plan and energy storage strategy based on historical data and forecast information; an intraday real-time rolling correction layer for dynamically adjusting the power generation plan formulated by the day-ahead optimization scheduling layer based on real-time monitoring data; a local execution layer for executing control commands issued from the upper layer and collecting and uploading the operating status and key parameters of local equipment; a reinforcement learning module for optimizing load response priority strategies by learning from historical data and real-time feedback, and uploading the optimized strategies as input parameters to the day-ahead optimization scheduling layer; and an edge computing module deployed at key nodes of the microgrid for enabling self-healing control of microgrid faults. This technical solution can improve the operating efficiency and reliability of microgrids.

The present application relates to a microgrid adaptive scheduling algorithm automatic discovery method based on meta-learning; first, a microgrid meta-learning framework is constructed, a scheduling rule search space is defined, including an agent network output form and a meta-network structure, and a self-healing mechanism is designed to realize self-learning ability; second, a multi-microgrid parallel training mechanism is designed, multiple agents interact with their respective environments simultaneously, and a unified scheduling rule is used for learning; third, a meta-gradient optimization method is used, the agent update process is penetrated through back propagation, and the meta-network parameters are optimized to maximize the cumulative income of the agent group; finally, the discovered scheduling algorithm is evaluated in microgrid environments of different scales and different equipment configurations to verify its generalization performance and adaptive ability; the present application can automatically discover the scheduling algorithm, breaks through the limitations of traditional methods relying on artificial design, has strong generalization ability and the ability to quickly adapt to new environments, and can be applied to microgrid systems of different scales and configurations.

This invention discloses a strip-shaped embedded micro grid-connected inverter at the edge of a photovoltaic panel, comprising a matching strip-shaped housing and internal circuitry. The strip-shaped housing is embedded within the metal frame of a single photovoltaic panel. The internal circuitry sequentially includes a PV-end filter, an isolated non-resonant high-frequency boost DC-DC converter, a full-bridge inverter, and a grid-connected filter. It employs a non-resonant linear soft-switching topology, configured to enable all internal switches to achieve non-resonant zero-current soft-conduction and all diodes to achieve non-resonant zero-current soft-turn-off. This invention discloses a strip-shaped embedded micro grid-connected inverter at the edge of a photovoltaic panel, achieving deep integration with the photovoltaic panel frame through a strip-shaped structure design and utilizing the linear variation characteristic of transformer leakage inductance current to achieve non-resonant linear soft-switching across the entire power range.

This invention relates to the field of microgrid control technology and discloses a method and system for synchronizing microgrid inverters based on satellite time synchronization. The method includes at least: receiving a satellite time synchronization signal and preprocessing it to obtain a global time reference signal; acquiring real-time operating parameters of each inverter within the microgrid and calculating the real-time phase offset of each inverter based on the global time reference signal and each real-time operating parameter; generating a synchronization phase angle for the corresponding inverter based on each real-time phase offset; acquiring real-time interaction data of each inverter and updating each initial phase adjustment command based on the real-time interaction data and the global synchronization phase angle to obtain a target phase adjustment command for the corresponding inverter; and adjusting the output voltage of the corresponding inverter using each target phase adjustment command. This invention improves the synchronization control accuracy and operational stability of multi-inverter clusters in microgrids.

The application discloses a micro-grid distributed cooperative control method and system based on FCS-MPC and improved finite-time consensus, relates to the technical field of power system control, and comprises the following steps: configuring a hybrid energy storage system of super capacitors and storage batteries in a photovoltaic power generation unit and a fan power generation unit respectively, and constructing a distributed power generation unit; utilizing FCS-MPC to optimize the switching state of a Boost voltage-boosting circuit, a hybrid energy storage bidirectional DC / DC converter and a two-level inverter, and performing voltage dynamic regulation and fluctuation correction of a direct-current bus; constructing a micro-grid; performing secondary frequency recovery control and active power proportional distribution based on a secondary frequency recovery control strategy of the improved finite-time consensus theory; and performing secondary voltage recovery control and reactive power proportional distribution based on a secondary voltage recovery control strategy of the improved finite-time consensus theory. The application improves the operation stability and power quality of the micro-grid under high renewable energy penetration.