3133 results about "Micro grid" patented technology

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention discloses an optical storage and charging integrated micro-grid energy management system, which relates to the related technical field of micro-grids and comprises an electric energy collection layer, a digital twinborn layer, a collaborative optimization layer, an application execution layer, a photovoltaic power generation unit, an energy storage unit, a charging load unit and a distributed measurement and control terminal. The invention further discloses an energy management method of the photovoltaic, storage and charging integrated micro-grid. The energy management method comprises the steps of data acquisition, digital twin modeling, multi-target strategy generation and optimization, strategy evaluation and screening, strategy issuing and execution and closed-loop feedback and dynamic correction. According to the invention, through short-term prediction of a digital twinborn layer and real-time generation of a charging and discharging strategy by a collaborative optimization layer, fluctuating renewable energy sources are preferentially consumed, the light abandoning rate is reduced, energy storage charging is automatically triggered in an illumination peak period, and power overflow is avoided; based on the simulation result of the digital twinborn model, the power distribution of energy storage and load is dynamically adjusted, so that the photovoltaic utilization rate is improved, and the dependence on a traditional power grid is reduced.

The invention provides a micro-grid fault diagnosis and dynamic recovery method based on deep reinforcement learning, and the method focuses on the multi-modal features of key nodes through a graph attention mechanism, extracts fault features through a multi-layer graph attention layer, and captures the spatial dependence relation of micro-grid nodes to recognize a fault propagation path. Meanwhile, spatial features and historical multi-modal data are fused with the help of a gating circulation unit, space-time joint feature representation is constructed, pre-fault symptom time sequence evolution is captured, intermittency and early fault detection capacity are enhanced, the multi-modal feature data fusion problem is solved, and high-precision fault diagnosis is achieved. A knowledge distillation technology is adopted to deploy a lightweight student model at edge equipment, millisecond-level emergency response is realized, fault diffusion is prevented, and meanwhile, the accuracy of diagnosis and repair strategies is guaranteed. The optimal repair strategy is generated at the cloud through the teacher model by using the global data, the system can adapt to the topological change of the micro-grid and novel faults, and the fault processing capability is continuously improved.

The embodiment of the invention discloses a park load prediction method and device based on multi-modal data and a medium, and relates to the technical field of micro-grids, and the method comprises the steps: collecting multi-modal micro-grid data through a multi-source heterogeneous sensor network disposed at a power distribution node, the multi-mode micro-grid data comprises electrical measurement data, environment monitoring data and equipment state data; constructing a dynamic space-time incidence matrix according to the multi-modal micro-grid data, and performing feature extraction based on the dynamic space-time incidence matrix through a space-time diagram convolutional network deployed at an edge calculation node to determine a space-time feature vector; and inputting the spatio-temporal feature vector into a time sequence fusion prediction model, outputting a load prediction result, and generating an optical storage cooperative scheduling instruction according to the load prediction result, the load prediction result including a load prediction value and a confidence interval.

The invention provides a micro-grid energy management method and system based on deep reinforcement learning, and relates to the technical field of power grids, and the method comprises the steps: constructing a dual-time scale deep reinforcement learning model which comprises a day-ahead scheduling sub-network and a real-time scheduling sub-network; the day-ahead scheduling sub-network predicts micro-grid operation strategies at a plurality of time points in the future based on the long short-term memory network; the real-time scheduling sub-network is based on a depth deterministic strategy gradient algorithm, real-time state data and a day-ahead scheduling prediction result are fused to construct an evaluation function, an instant reward value is calculated, and renewable energy power generation, energy storage charging and discharging and an external power grid electricity purchasing and selling power adjustment instruction are optimized online. According to the invention, through dual-time-scale collaborative optimization, the energy management efficiency and economic benefits of the micro-grid are improved, and the operation stability of the micro-grid is enhanced.

A tracking and adaptive compensation method for power fluctuation of a pulse load of a ship is provided. The method includes: based on a microgrid topology model of the ship, a pulse load model of the pulse load, and an interharmonic power and pulse parameter relationship model, performing power fluctuation tracking, frequency selection and a power spectrum analysis to obtain filtering parameters of a low-pass filter and filtering parameters of a band-pass filter; performing waveform adaptation to eliminate a phase delay caused by the low-pass filter and the band-pass filter, and obtaining a control command for a power compensation strategy; and generating, by a power loop, a pulse width modulation (PWM) signal to control a switching tube in a bidirectional direct current (DC) / DC converter of each of an energy storage battery pack and a supercapacitor of a hybrid energy storage system.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

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 discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

The invention provides an anti-islanding protection method based on cloud edge collaboration, and the method comprises the steps: obtaining historical fault data, carrying out the data processing of the historical fault data, obtaining a fault mode discrimination result, judging whether a micro-grid has an islanding operation risk or not through combining the topological structure information of a power grid, real-time operation data, and load demand prediction data, and obtaining an islanding protection result. If so, acquiring power output fluctuation data; the output power of the power supply and the load demand power are monitored in real time, if the output fluctuation of the power supply exceeds a voltage protection constant value or the load demand power exceeds a frequency protection constant value, the edge computing gateway starts a local anti-islanding control strategy, and meanwhile, the charging and discharging power of the power distribution network is adjusted; in the anti-islanding control strategy execution process, the edge computing gateway continuously collects real-time operation state data of the micro-grid, the power supply duration, the electric energy quality and the standby capacity of the micro-grid under islanding operation are evaluated according to the operation state data, and the multi-target optimization model and the operation constraint condition of the micro-grid are dynamically adjusted.

The invention discloses a micro-grid anti-countercurrent control method and system based on multi-objective optimization, and the method comprises the steps: generating a dynamic load prediction result according to the power grid side voltage, user side load power and energy storage system charge state data collected by an energy management system in real time; based on the dynamic load prediction result, obtaining a countercurrent risk quantitative index; generating a corresponding dynamic anti-reflux threshold curve according to the reflux risk quantitative index; the dynamic anti-reflux threshold curve is corrected through a real-time collaborative verification module, and an anti-disturbance optimization threshold is generated; and converting the anti-disturbance optimization threshold value into a PCS power regulation and control instruction, synchronously displaying a threshold value execution state and reverse feed early warning information on an energy management interface, and triggering adaptive power clamping protection when detecting that the voltage is out of limit or the power is reverse. According to the embodiment of the invention, the intelligent level of countercurrent prevention and control of the micro-grid can be improved, and safe and stable operation of the grid is guaranteed.

The invention belongs to the field of microgrid resource capacity optimization. The invention provides a power distribution network wind and light storage capacity optimization method considering multi-microgrid energy storage cooperation. The method comprises the following steps: step 1, establishing a wind and light combined operation power information data set considering spatial correlation during multi-microgrid source load fluctuation; and step 2, multi-microgrid wind and light storage capacity configuration optimization is realized by using a reinforcement learning algorithm. And step 3, complementing the wind-light fluctuation scene with few samples by using a transfer learning algorithm. Based on deep fusion space-time correlation modeling, multi-agent reinforcement learning and cross-domain transfer learning, a multi-microgrid energy storage collaborative optimization framework with dynamic adaptive capacity is provided. According to the method, the deep association rule of the multi-dimensional operation data of the micro-grid group can be analyzed, global optimal capacity configuration is realized through a coevolution mechanism of an intelligent algorithm, and a brand new solution is provided for solving the problem of power distribution network optimization under high-proportion new energy access.

The invention discloses a micro-grid intelligent economic regulation and control system and method based on carbon emission optimization, and relates to the technical field of economic regulation and control. The method comprises the following steps: deploying an Internet of Things sensor to collect photovoltaic generating capacity, energy storage SOC, load demand and power grid carbon intensity data in real time, and transmitting the data to an edge computing node through a 5G / optical fiber hybrid communication network; preprocessing the data by adopting wavelet transform, and establishing a time sequence prediction model of photovoltaic output / load demand; constructing a dynamic carbon flow tracking matrix; performing decision optimization according to the output dynamic carbon emission spectrum and the power carbon flow traceability data; and the edge node issues a control instruction through a Modbus-TCP protocol to carry out economic regulation and control. According to the method, multi-source data are collected in real time, a minute-level carbon emission equation is established based on a dynamic carbon flow tracking model, dynamically-changed energy carbon emission factors and line transmission loss are fused, and the micro-grid carbon footprint is accurately quantified.

The invention discloses an optimal scheduling method and system for a wind-light-hydrogen storage micro-grid, and relates to the field of wind-light-hydrogen storage micro-grids, and the method comprises the steps: obtaining multi-source time sequence data, and carrying out the combined denoising and dynamic time alignment to generate a standardized input sequence; adopting a quantile regression model fused with a space-time attention mechanism to output a power prediction interval of wind and light output and load demand in a future time period; constructing a layered multi-objective optimization model; an improved adaptive particle swarm optimization algorithm is adopted to solve the layered multi-objective optimization model, and an equipment scheduling instruction set is generated; and dynamic correction is carried out, and micro-grid instruction distribution is carried out according to the equipment response priority. According to the method, multi-target conflicts such as power balance, equipment loss and energy efficiency are effectively balanced through multi-source data efficient preprocessing, space-time joint prediction interval generation and hierarchical multi-target optimization, and efficient and stable operation of the wind-light-hydrogen storage micro-grid is achieved.

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system based on dynamic optimization control and a multi-mode energy scheduling method. Comprising a renewable energy power generation unit which comprises at least two distributed power supplies with different power generation characteristics; the multi-stage hydrogen production device is configured to receive the electric energy output of the renewable energy power generation unit and perform proton exchange membrane water electrolysis hydrogen production; the hybrid energy storage system comprises a super capacitor bank and a lithium ion battery pack which are connected in parallel to the direct current bus through a bidirectional DC / DC converter; the dynamic optimization controller is internally provided with a multi-time-scale coordination algorithm and is used for acquiring meteorological prediction data, load demand data and an energy storage SOC state in real time; the multi-mode scheduling module comprises three working modes including an island operation mode, a grid-connected operation mode and an emergency standby mode; according to the scheme, hydrogen production economy, equipment durability and power supply reliability can be considered at the same time.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention provides a micro-grid group-containing AI active distribution network scheduling optimization method, a medium and a system, and belongs to the technical field of power grid scheduling. The method comprises the following steps: firstly, constructing a micro-grid group and main and distribution network interaction model, and determining boundary constraints; predicting key parameters of the micro-grid group by using deep reinforcement learning; constructing an active distribution network power balance equation and topology constraint conditions; solving by adopting mixed integer programming to obtain power flow distribution of the distribution network; constructing a power grid dispatching optimization objective function based on a quantum evolutionary algorithm; processing multi-source data by using an MGPN deep neural network to output an optimal scheduling strategy; monitoring a running state verification effect in real time through a state estimation technology; updating the strategy in real time by applying a rolling optimization mechanism; and establishing an evaluation system to dynamically optimize neural network model parameters, realizing efficient collaborative scheduling of the micro-grid group and the active power distribution network, and solving the technical problem of low distributed energy consumption rate in the collaborative scheduling optimization process of the micro-grid group and the active power distribution network.

The invention relates to the technical field of power grid self-healing control, and discloses a power grid self-healing control intelligent optimization method and system, and the method comprises the steps: obtaining the topological structure information of a power grid, dividing n power grid toughness evaluation regions according to the topological structure information of the power grid, calculating a hierarchical toughness evaluation index, and forming a comprehensive toughness evaluation index according to the hierarchical toughness evaluation index; a self-healing control intelligent optimization model is constructed based on the comprehensive toughness evaluation indexes, a self-healing control optimal solution is obtained by solving the self-healing control intelligent optimization model, the self-healing control optimal solution is dynamically calibrated, and a self-healing control strategy is generated; according to the method, the topological structure, the historical fault data and the real-time operation state of the power grid are comprehensively considered, a layered evaluation and dynamic calibration combined mode is adopted, the intelligent level and adaptability of self-healing control of the intelligent micro-grid are improved, the toughness and recovery capacity of the micro-grid for coping with complex faults are enhanced, and safe, reliable and efficient operation of the power grid is guaranteed.

A method and system for robust optimization of microgrid scheduling, relating to the technical field of microgrid scheduling. The method comprises: constructing a multi-interval uncertainty set by means of an uncertainty prediction parameter (S1); on the basis of the established multi-interval uncertainty set, constructing a robust scheduling model of a microgrid (S2); and using a column constraint generation algorithm to iteratively solve the constructed robust scheduling model, to obtain a net load curve and an operation plan for energy output (S3). In the present invention, on the basis of traditional single-interval robust optimization, a multi-interval uncertainty set is constructed on the basis of wind power prediction data, a multi-interval two-stage robust optimization model is established on the basis of the foregoing, and the conservative nature of single-interval robustness is reduced. A nested column and a constraint generation algorithm are used for solving. In the first stage, using minimum net load fluctuation as a goal, planning is carried out on the basis of the prediction data, and in the second stage, considering the uncertainty of wind power, wind power output in a worst-case scenario is searched for, and the policy of the first stage is adjusted, so that the stability of the microgrid is ensured.

The invention provides a microgrid power load prediction and dynamic control method based on CNN-LSTM-AM, and relates to the technical field of intelligent control of a power system. According to the method, multi-source data is collected, data preprocessing is carried out, a CNN-LSTM-AM hybrid prediction model is constructed, and a CNN layer comprises a double-branch multi-scale one-dimensional convolution kernel; the output of the input layer and the output of the LSTM layer are connected to the DSTCW module, the DSTCW module outputs weighted load characteristics and photovoltaic / wind power characteristics, the charging and discharging priority is optimized based on the energy storage SOC and the real-time electricity price, a multi-stage cooperative stability control strategy is triggered through a closed-loop control link, and dynamic control is achieved. Wide-area spatial features of distributed photovoltaic / wind power are extracted through a multi-scale one-dimensional convolution kernel, and the control response speed is increased by combining the spatial-temporal relevance between a dynamic attention mechanism focusing load and renewable energy sources; and the load power and the photovoltaic / wind power output are synchronously predicted by adopting dual-task output, so that the power grid stability and the control real-time performance in a high-proportion renewable energy scene are improved.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention discloses a hybrid micro-grid load control method, system and device and a storage medium, and the method comprises the steps: carrying out the feature extraction of historical operation data in a hybrid micro-grid, and obtaining a power factor distribution set of multiple types of loads; performing time sequence analysis on the power factor distribution set, and performing fusion processing on an increment information sequence obtained based on an analysis result to obtain load dynamic power characteristic description data; performing parameter correction on the load power simulation model based on the load dynamic power characteristic description data to obtain a first digital twinborn model; performing secondary correction on the first digital twinborn model based on the load power characteristic subset to obtain a second digital twinborn model; and inputting the real-time operation data into the second digital twinborn model to obtain second reactive power demand data, and executing a load control instruction of the hybrid microgrid generated by the second reactive power demand data. According to the method provided by the invention, the operation stability of the hybrid microgrid is improved.

The invention discloses a micro-grid dynamic collaborative scheduling method based on multi-modal reinforcement learning, and relates to the technical field of micro-grid control, and the method comprises the steps: constructing a multi-modal state space of a micro-grid, designing a layered reward function, and dynamically adjusting the reward weight of each layer of the layered reward function through employing a self-adaptive weight algorithm; dynamically identifying a node connection relationship by using a graph neural network, constructing a microgrid node connection graph to identify an operation mode of the microgrid, updating the node connection relationship in real time, and optimizing a distributed power supply output distribution strategy of the microgrid based on an attention mechanism of the graph neural network; and updating the overall model of the micro-grid by using a cloud edge coordination mechanism. According to the invention, the capability of coping with uncertainty of the micro-grid can be enhanced, and the intelligent operation level of the micro-grid is improved.

The invention provides a detection method and system for a photovoltaic micro-grid charging pile and a storage medium, and belongs to the technical field of photovoltaic micro-grid charging pile detection, and the detection method comprises the steps: S1, carrying out the collaborative collection of multi-dimensional data; s2, spatio-temporal feature fusion and correlation modeling; s3, multi-physics field coupling health degree evaluation; and S4, performing fault diagnosis. According to the invention, integral systematic high-efficiency detection can be carried out.

The invention discloses a micro-grid electric energy quality global optimization method, which comprises the steps of collecting real-time data, dividing the real-time data into a plurality of segments according to frequency, and calculating harmonic compensation capacity of each frequency band according to the real-time data; according to the harmonic compensation capacity obtained through calculation, priority distribution is carried out on the compensation sequence of the polluted nodes in the micro-grid; the virtual impedance and the injection damping resistance of the inverter are adjusted, and the harmonic current and the resonance phenomenon in the micro-grid are suppressed in different frequency bands; and according to the adjusted virtual impedance and damping resistance, updating a node admittance matrix, monitoring the compensation capacity of the inverter in real time, comparing real-time monitoring data with a threshold value, if the real-time monitoring data exceed the threshold value, correcting the inverter, otherwise, not changing. Through frequency-band-divided control, pertinence and effect of harmonic suppression are improved, frequency-band-divided suppression of micro-grid harmonic and high-frequency resonance is realized, residual capacity of an inverter and harmonic compensation requirements are dynamically matched, and safe and stable operation of a system is ensured.

The invention discloses a power grid intelligent scheduling decision-making system and method based on multi-source heterogeneous data fusion, and relates to the technical field of power grid intelligent scheduling, the power grid intelligent scheduling decision-making method based on multi-source heterogeneous data fusion specifically comprises the following steps: 1, collecting multi-source heterogeneous data, constructing a meteorological feature vector set, and predicting power grid load change; the method comprises the following steps of: 1, outputting a load prediction value, 2, fusing the load prediction value and an equipment health coefficient to construct a double-risk collaborative analysis matrix, generating a risk entropy value and mapping the risk entropy value into a standard risk mode, and 3, executing generator output pre-adjustment, energy storage charging and discharging control and interruptible load management in a grading manner according to the standard risk mode. Micro-grid resources are coordinated through the block chain; according to the method, the demand side prediction risk and the equipment health risk are orthogonally decomposed into multiple state modes by constructing a two-dimensional risk collaborative decision architecture, and the one-sidedness of a single risk factor decision is avoided.

The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery, and belongs to the field of active power distribution network operation analysis. The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery. The method comprises the following steps: establishing an energy storage system refined operation model in a wind-light storage micro-grid; on the premise of maximum income and maximum new energy consumption, the method has the beneficial effects that the new energy power output fluctuation is responded in time, so that the charge and discharge control of the energy storage system is efficient and accurate, and the purposes of improving the energy storage utilization efficiency, the charge and discharge control accuracy and the power output stability are achieved; according to the improved multi-objective grey wolf optimization algorithm, an initial population strategy, a population individual iteration updating strategy and a multi-objective optimal solution solving mechanism are improved, and the convergence speed, the solution set diversity and the calculation efficiency of the multi-objective grey wolf optimization algorithm are improved through multi-strategy cooperation; the problems of excessive uncertainty and single target of the existing wind and light storage micro-grid are effectively solved.

The embodiment of the invention discloses a collaborative optimization method and device for a distributed micro-grid and a medium, and relates to the technical field of energy management, and the method comprises the steps: carrying out the real-time data collection through a multi-element sensor network disposed at each distributed energy node in advance, so as to obtain the real-time energy data corresponding to each distributed energy node; according to the edge node corresponding to each distributed energy node, carbon emission of the distributed energy nodes is predicted by using the real-time energy data, and node-level predicted carbon emission parameters are determined, so that single-node optimization reference information corresponding to each distributed energy node is determined; through a preset global center node, collecting single node optimization information corresponding to each distributed energy node for aggregation, and determining global model gradient information; and based on the global model gradient information and the single-node model gradient information, carrying out collaborative optimization on the single-node optimization strategy to determine a collaborative optimization strategy corresponding to each edge node.