3728 results about "Power flow" patented technology

The invention discloses an intelligent power distribution network line state real-time monitoring, analysis and evaluation system, and relates to the technical field of power system monitoring and control, the intelligent power distribution network line state real-time monitoring, analysis and evaluation system is constructed, data is collected in multiple dimensions, features are extracted through edge calculation, data is screened, a dynamic threshold model is constructed at a cloud end, and abnormity is identified. The method has the advantages that a three-dimensional digital twin network is constructed based on GIS data and an SCADA system through the power grid topology modeling module, and a unique'topology fingerprint 'containing electrical connection, physical position and logic control relation is generated for each piece of equipment; an upstream and downstream equipment list, a load influence range and a power flow transfer path are marked, and an association state evaluation module adopts a graph convolutional neural network and can accurately capture electrical coupling, physical proximity and historical fault association effects among equipment.

The invention relates to the technical field of power dispatching, in particular to a digital twin energy management method and system for source-network-load-storage cooperative dispatching, and the method comprises the steps: collecting source-network-load-storage multi-dimensional space-time operation data, and extracting space-time coupling features through a graph convolution-long and short-term memory network; establishing a simulation model of a digital twin environment, simulating an uncertain operation condition by using a Monte Carlo scene generator, and processing a power flow constraint by using a second-order cone relaxation technology; training an energy storage scheduling agent in a digital twin environment, and learning an energy storage charging and discharging strategy through a near-end strategy optimization algorithm; designing a source-network-load-storage hierarchical collaborative optimization framework, optimizing power output and load distribution by using an improved particle swarm optimization algorithm on the upper layer, and solving power flow distribution by using an alternating direction multiplier method on the lower layer; and establishing a self-adaptive feedback correction mechanism, and dynamically adjusting a cooperative scheduling strategy. According to the invention, intelligent collaborative scheduling of source network load storage is realized, and the operation efficiency and stability of a power system are improved.

The invention discloses a power grid power flow risk control method based on topology analysis, and relates to the technical field of power flow risk control, and the method comprises the following steps: obtaining first data of a power grid in real time, and constructing a node admittance matrix; when a switch displacement signal or distributed power supply power mutation is detected, topology reconstruction is carried out based on a node admittance matrix, and a dynamic topological graph is generated; establishing a fault current characteristic matrix based on the dynamic topological graph in combination with the bidirectional power flow characteristics of the active power grid; establishing an adaptive risk early warning model based on the dynamic topological graph and the fault current characteristic matrix, and outputting the overload probability and the voltage out-of-limit risk index of each branch; and performing analysis according to the branch overload probability and the voltage out-of-limit risk index, and generating a multi-target optimization control instruction set. Through adaptive topology adjustment, load flow calculation and a risk early warning model, automatic power grid optimization control can be realized, and in combination with convergence verification of a virtual impedance observer, effectiveness of topology adjustment and control operation is ensured.

The invention relates to the technical field of operation prediction, in particular to an optical storage flexible direct current operation prediction method based on deep learning, and the method achieves the precise evaluation of photovoltaic power fluctuation through the analysis of historical operation data and the combination of a power state conversion matrix, guarantees that the energy storage charging and discharging rate can be matched with the load demand distribution, and improves the prediction precision. Power scheduling is optimized, topology analysis and power flow path identification are carried out, topology weight calculation is carried out by using a graph convolutional network, power flow can be dynamically adjusted, the adaptability of an operation mode is improved, the influence of power fluctuation on system stability is reduced, power matching is carried out by adopting an adversarial generative network, photovoltaic output is optimized, and the system stability is improved. The energy storage release path is more reasonable, the power loss is reduced, the overall power distribution efficiency is improved, and the load compensation strategy is optimized through short-period fluctuation detection and long-period trend fitting. And variable correction is performed based on error calculation and anomaly detection, so that the reliability of regulation and control response is improved.

Disclosed is a method and system for quickly identifying and responding weak point-associated power flow based on a sensitivity factor, relating to the technical field of dispatching automation of power system. The method includes: parameter data is acquired, and a transfer factor (SF) matrix is established; consumption and accommodation of new energy of a power distribution network is evaluated, and a line with power flow exceeding a limit is evaluated; and flexible load in the power distribution network is dispatched based on the sensitivity factor, and safety verification is carried out on the result. The present invention identifies a blocking line of the power distribution network, the situation of out of limit of the line is eliminated, so that the safety of the power gird is ensured, and the new energy is consumed and accommodated.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The present invention relates to the technical field of power system dispatching automation. Disclosed are a rapid identification and response method and system for a weak point-associated power flow based on a sensitivity factor. The method comprises: acquiring parameter data, and establishing a transfer factor matrix; assessing renewable energy integration of a distribution network and assessing a power flow over-limit line; and dispatching flexible loads in the distribution network on the basis of a sensitivity factor, and carrying out safety verification on a dispatching result. According to the rapid identification and response method for a weak point-associated power flow based on a sensitivity factor provided by the present invention, a weak point in new energy integration of the distribution network is identified, to obtain a blocking line that hinders new energy integration, providing a foundation for subsequent precise dispatching control for flexible loads. Sensitivity analysis is carried out on a weak point-associated power flow and flexible load user nodes on the basis of the sensitivity factor, and the line over-limit condition is eliminated by dispatching flexible loads of corresponding nodes, so that the power grid safety is ensured and new energy integration is implemented.

A right-hand circularly-polarized patch antenna comprising a ground plane and a patch connected to each other with one or more wires for which the wire shape and location of the end points are selected such that they do not cause an antenna mismatch, and the electrical current carried in the wires produces an extra electromagnetic field subtracted from the patch field in the nadir direction.

The embodiment of the invention provides a power distribution network multi-zone-area collaborative optimization regulation and control method and device adapting to topological changes. The method comprises the steps of performing cluster division on a power distribution network; constructing an optimization model according to a cluster division result; respectively constructing a dynamic grouping Markov decision model of each transformer area by taking each transformer area as an intelligent agent; solving the dynamic grouping Markov decision model based on the optimization model to obtain a corresponding power distribution network multi-transformer-area collaborative optimization regulation and control strategy, and regulating and controlling the power distribution network according to the regulation and control strategy; according to the method, dynamic cluster division and distributed autonomous optimization can be combined, and the SLM algorithm is adopted to enhance the station area coordination capability and reduce the cross-cluster power flow influence; according to the invention, the feature extraction network is introduced to adapt to topological change, and the generation of the regulation strategy is optimized; communication dependence is reduced through centralized training and decentralized execution, and efficient voltage regulation and control in a low-perceptibility scene are achieved in a large-scale power distribution network complex environment.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention relates to the technical field of electrical engineering, and provides an electric energy storage system optimization scheduling method and system based on reinforcement learning, so as to solve the problems that it is difficult to effectively evaluate and control the battery aging risk in the frequent charging and discharging process and it is difficult to meet the real-time scheduling requirement in the edge side low computing power environment. The method comprises the steps of obtaining battery charging state and health state data in an electric energy storage system and power grid load and power grid electricity price data of an industrial and commercial park, and generating a state input data set; using the reinforcement learning agent to obtain a charging and discharging scheduling instruction; performing power distribution on the electric energy storage system, the multi-port converter power interface and the park load to generate multi-source cooperative power flow data; in combination with historical operation data, a reinforcement learning agent is utilized to optimize the charging and discharging scheduling instruction; and performing optimal scheduling of the electric power energy storage system according to the optimized instruction. According to the invention, efficient, intelligent and adaptive optimization scheduling of the power energy storage system in the industrial and commercial park is realized.

A new-power-system power grid restoration method and apparatus based on second-order cone transformation. The method comprises: acquiring power data of a new power system (101); on the basis of the power data and taking the output uncertainty of a new energy unit into consideration, constructing a mixed-integer nonlinear two-stage robust optimization model based on a multi-state uncertainty set (102), wherein the model comprises an alternating-current power flow constraint; using a second-order cone relaxation technique to simplify the alternating-current power flow constraint, and converting the mixed-integer nonlinear two-stage robust optimization model into a mixed-integer second-order cone two-stage robust optimization model (103); decomposing the mixed-integer second-order cone two-stage robust optimization model into a main question and a sub-question, and solving the main question and the sub-question, so as to obtain an optimization result (104); and on the basis of the optimization result, generating a power grid restoration scheme for the new power system (105).

The invention discloses an electric energy meter error real-time monitoring and intelligent calibration system and method, and relates to the technical field of electric energy meter calibration, and the system comprises a target power data calculation module which is used for collecting voltage and current signals in real time, and carrying out the calculation to obtain target power data; the reference power data calculation module is used for performing real-time calculation to obtain reference power data; the error vector acquisition module is used for acquiring an error vector; the error prediction result generation module is used for carrying out modeling analysis on the error vector and generating an error prediction result; and the target electric energy value output module is used for outputting the target electric energy value. The technical problem that the metering accuracy is insufficient due to the fact that the electric energy meter lacks an effective error analysis and prediction means and a dynamic calibration mechanism in a bidirectional power flow scene in the prior art is solved, and the purposes of real-time monitoring, error analysis and prediction and dynamic calibration of the forward and reverse power data of the electric energy meter are achieved. And the metering accuracy of the electric energy meter in a bidirectional power flow scene is improved.

The invention provides an active transformer area voltage out-of-limit early warning and photovoltaic bearing capacity evaluation method, which comprises the following steps: acquiring multi-source data of a power distribution transformer area, and generating a standardized multi-source time sequence data set through time alignment processing, data quality evaluation and feature fusion; extracting voltage out-of-limit features based on the time sequence similarity matrix and the node influence matrix; the periodicity, volatility and randomness characteristics of photovoltaic output are analyzed, a coupling relation matrix and a sensitivity vector are established, and an influence evaluation model is constructed; performing out-of-limit early warning based on an early warning index system and a deep learning model; and establishing a multi-dimensional evaluation index system for carrying out bearing capacity evaluation in combination with power flow analysis and the dynamic influence factors. Through multi-source data fusion and multi-dimensional feature analysis, the accuracy of voltage out-of-limit early warning is improved, accurate evaluation of the photovoltaic bearing capacity is realized, and effective technical support is provided for safe operation of the power distribution network.

The invention discloses a power distribution area flexible interconnection group planning method for mutual energy assistance, and relates to the technical field of power distribution network planning, and the method comprises the steps: screening out area pairs meeting conditions based on the topological parameters of a power distribution network, area loads and new energy output data, and constructing an initial interconnection set; constructing an initial two-stage opportunity constraint optimization model comprising a planning stage and an operation stage based on the set; in the planning stage, an interconnection strategy and a new energy consumption rate are determined by taking minimization of investment cost and punishment cost as a target; the strategy feasibility is verified through a random scene in the operation stage; carrying out polyhedral approximate linearization processing on nonlinear constraints in the model, reconstructing a power flow model into a compact form, converting probabilistic opportunity constraints into deterministic bilinear constraints by using a sampling average approximation method, forming a deterministic approximation model, and carrying out iterative solution through a column constraint generation decomposition algorithm to obtain a target interconnection grouping scheme; and high-efficiency energy mutual aid and load balancing between power distribution stations are realized.

The invention discloses a power distribution network voltage control method based on distributed photovoltaic active-reactive cooperation, and the method comprises the steps: analyzing the influence of reactive / active injection on voltage stability through building a sensitivity matrix, deducing a critical control gain constraint condition, and guaranteeing the static stability of the voltage of a power distribution network; a photovoltaic inverter active / reactive cooperative control general model suitable for different scenes is constructed based on an intra-day scheduling and real-time control two-stage framework, and control curve parameters of each node photovoltaic inverter are optimized by taking minimization of network loss cost, active reduction cost, inverter voltage regulation cost and voltage deviation as targets in the intra-day scheduling stage; in the real-time control stage, active power reduction and reactive power regulation are dynamically coordinated based on an optimal control curve, reactive power margin is preferentially utilized to suppress voltage rise, and power flow distribution is directly regulated through active power reduction when reactive power capacity is saturated. According to the method, the safety and economy of the power grid are considered, and theoretical support and a practical solution are provided for voltage control of the high-permeability photovoltaic power distribution network.

The invention discloses a power distribution network harmonic suppression and heat loss evaluation method and system, and relates to the technical field of electric energy quality optimization and heat loss evaluation, and the method comprises the steps: collecting a power distribution network signal, extracting harmonic features, and calculating the harmonic contribution degree of each load based on a harmonic power flow; cable additional heat loss caused by harmonic current is calculated, and cable temperature prediction is carried out based on a transient heat conduction equation; and dynamically adjusting parameters of the harmonic compensation device according to harmonic treatment and heat loss evaluation results, and optimizing load scheduling. According to the method, more accurate harmonic source identification and harmonic power flow calculation are provided, and the harmonic treatment accuracy is improved; in cable heat loss evaluation, eddy current loss modeling, heat conduction analysis and dynamic temperature prediction are introduced, so that the operation safety of the cable is enhanced; dynamic harmonic suppression optimization and load scheduling are combined, a closed-loop regulation mechanism is formed, the electric energy quality is improved, and meanwhile, the cable overheating risk is effectively avoided.

The invention provides a power grid cable loss analysis method and device and a storage medium, and the method comprises the steps: obtaining cable topology data, extracting parameter characteristics, and generating a parameter database; performing matrix conversion based on the parameter database to establish a network calculation model, executing load flow calculation, and outputting voltage power distribution data; establishing a temperature and loss coupling relation model according to the voltage power distribution data, calculating cable temperature distribution, and generating time-varying loss data; and performing multi-dimensional feature analysis on the time-varying loss data, calculating a loss risk index, identifying loss hotspots and generating an optimization scheme. According to the method, the dynamic relation model of the cable parameters and the loss is established, and the cable shielding layer characteristics and the temperature change factors are combined, so that accurate analysis of the power grid cable loss is realized, technical support is provided for identifying loss hotspots and formulating a targeted optimization scheme, and the power grid loss calculation deviation is effectively reduced.

The invention discloses an electric power system climbing demand evaluation and optimization method considering load side resources, and the method comprises the steps: providing a calculation method for maximum upward and downward climbing demands in different time periods, including a calculation method for deterministic and uncertain climbing demands, in order to effectively cope with the fluctuation and uncertainty of loads or new energy; a climbing demand optimization model which takes minimization of the uncertainty climbing demand as an objective function and takes constraint conditions such as load adjusting quantity and demand response into consideration is provided; a gradeability optimization model which takes minimization of comprehensive consumables of the power system as an optimization target and considers constraints such as node balance and line power flow is provided; modeling is carried out on AMPL software, a solver GUROBI is called for solving, and an electric power system climbing demand evaluation and optimization scheme is obtained. The comprehensive operation consumables of the power system can be effectively reduced, and the net load peak load shifting effect on the power system is achieved, so that the operation reliability of the power system is improved, and the dispatching pressure of the power system is relieved.

The invention relates to the technical field of power grid optimization, in particular to a regional power grid reactive power optimization control method and system, and the method comprises the steps: building a dynamic response matrix according to key characteristic parameters of reactive power compensation equipment in a regional power grid; dynamically tracking the reactive power flow of the power grid according to the dynamic response matrix and the regional power grid line power flow operation state data to obtain a reactive power demand change trend; determining a target output adjustment value of each reactive compensation device according to the reactive demand change trend; generating an output adjustment control instruction according to the target output adjustment value; and adjusting the reactive power output of each reactive power compensation device according to the output adjustment control instruction, and dynamically adjusting the output limit of the reactive power compensation device according to the device operation state and the reactive power data of the reactive power compensation device in the reactive power output adjustment process of the reactive power compensation device. By dynamically adjusting the output limit of the reactive power compensation equipment, accurate control and optimal adjustment of the output of the reactive power compensation equipment are realized, and the reactive power compensation efficiency of a power grid is improved.

A collaborative resource allocation method for a virtual power plant cluster and a power distribution network comprises the steps that a virtual power plant cluster-power distribution network joint operation framework is constructed, a double-layer optimization model is constructed, and an upper-layer optimization model formulates an electric energy and carbon quota P2P transaction plan through a cooperative game between virtual power plants; the lower-layer optimization model solves the optimal power flow of the power distribution network based on the transaction plan and feeds back the electricity-carbon comprehensive price; and a cooperative game model is constructed, and the double-layer optimization model is solved by adopting an alternating direction multiplier algorithm, so that cooperative resource allocation is realized. According to the method, a joint operation framework of the virtual power plant cluster and the power distribution network is constructed, and a double-layer optimization model and an asymmetric Nash bargaining theory are combined, so that P2P transaction of electric energy and carbon quota is realized, and the optimal power flow of the power distribution network is optimized.

The invention discloses a power grid operation control method for realizing source-grid-load-storage cooperation, and relates to the technical field of power grid operation control, and the method comprises the steps: carrying out the ultra-short-term prediction of power grid operation data through LSTM, ARIMA, VAE, MDP and other models, covering the new energy output, power flow change, load demand and energy storage state, and carrying out the fusion of the day-ahead prediction and real-time prediction data, thereby achieving the real-time prediction of the power grid operation data. Optimizing a prediction result by using a weighting method; in the aspect of scheduling optimization, a scheduling optimization model is established according to prediction data, a day-ahead scheduling plan is calculated, the operation cost of a power grid is minimized, and meanwhile, source-side wind and light output, grid-side power flow regulation and control, load-side demand response and a storage-side charging and discharging strategy are optimized; scheduling optimization is introduced, so that the output of a conventional unit is reduced in a wind power high output period, and the new energy consumption rate is improved; in the real-time scheduling stage, dynamic optimization is carried out by adopting MPC in combination with a day-ahead scheduling plan and real-time prediction data; and when the wind power output is excessive, the energy storage equipment is controlled to absorb redundant electric energy.

Provided are a method for tracking carbon flow of a power system, a device and a medium. The method includes: acquiring related data of carbon flow tracking, where the related data include an injected active power sum column matrix of generator sets of all substations to be tracked, an injected carbon flow rate sum column matrix of generator sets of all substations to be tracked, an output power distribution matrix of a branch formed between substations to be tracked, and a power flow proportion distribution matrix of the branch formed between substations to be tracked; acquiring a carbon potential matrix EN of each substation to be tracked; and acquiring a carbon flow rate of each substation to be tracked, a carbon flow rate of the branch, and a carbon flow rate of an output load on the basis of the carbon potential matrix of each substation to be tracked.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The present invention relates to the field of backflow prevention monitoring technology, specifically a photovoltaic micro-inverter backflow prevention monitoring method, comprising the following steps: collecting real-time data between the inverter and the power grid through a Hall sensor, the real-time data including voltage, current and frequency, calculating the current power balance state according to the real-time data, and generating a real-time power state; based on the real-time power state, detecting whether there is a potential backflow risk. In the present invention, a Hall sensor is used to obtain the voltage, current and frequency data of the power grid and the inverter, and the backflow risk is calculated in combination with the power balance state, so that data collection can reflect instantaneous power flow and reduce the monitoring blind area caused by fixed collection intervals. Based on the analysis results of the power flow direction, the output power threshold is dynamically adjusted so that the inverter can respond quickly when the power feedback fluctuates, avoiding power feedback imbalance caused by unreasonable threshold setting.

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention discloses a harmonic traceability method based on a harmonic power direction of an electric power system, which belongs to the technical field of electric power quality of the electric power system and comprises the following steps of: constructing matrixes of fundamental waves and harmonic signals, initializing active and reactive power deviation values of nodes, and adopting a decoupling algorithm combined with a Jacobian matrix iteration method to carry out harmonic traceability. And sequentially calculating the fundamental wave voltage and each harmonic current, and finally obtaining the power flow direction and judging the harmonic source attribute. When harmonic power flows from a load to a power grid, the node is identified as a harmonic source; and when the harmonic power flows to the load from the power grid, identifying the node as a harmonic receiving point. An equivalent circuit model is used for separating harmonic influences of a linear load and a non-linear load, and a real-time monitoring technology is combined to realize dynamic tracking and accurate positioning of a harmonic source. The method is low in calculation complexity, excellent in convergence performance and suitable for a modern power distribution network scene where distributed energy and power electronic equipment are widely connected, the harmonic suppression efficiency is remarkably improved, and the safety and stability of power grid operation are ensured.

The invention provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

The invention belongs to the technical field of energy storage power stations, and particularly relates to an energy storage power station electric quantity optimization method based on graph calculation, which comprises the following steps: S1, predicting future power generation capacity and load demand of an energy storage power station based on graph calculation: acquiring historical power generation data and load data of a distributed power station in a grid-connected area of the energy storage power station, and external influence data, constructing a time-space diagram neural network model, and obtaining prediction results of the power generation data and the load data in each hour period of the next 24 hours; s2, calculating the power and load value of each node in the power grid through power flow analysis; and according to the predicted future power generation capacity and the load demand, the electric quantity needing to be provided or consumed by the energy storage power station is calculated. Optimization of energy of the power storage station is achieved, and the cost of the power storage station is reduced.

The invention discloses an island micro-grid operation scheduling method and device. According to the scheme, the method comprises the steps that power generation characteristic modeling is conducted on a distributed power source and an energy storage system of a diesel storage island micro-grid; constructing a function taking the minimum daily total operation cost as a target, wherein the function covers the operation cost of a diesel generator and an energy storage system and the pollutant treatment cost; establishing a power impact type load mathematical model including power characteristics, system dynamic frequency response characteristics, micro-grid dynamic frequency safety constraints and diesel generator frequency control constraints; establishing a micro-grid operation constraint model which has constraints such as power balance, Distflow power flow, energy storage, charging, discharging and capacity, diesel generator output and climbing rate; and solving the model by using a particle swarm optimization algorithm, generating a diesel generator output, energy storage system charging and discharging and frequency modulation reserve capacity distribution strategy, and obtaining an island microgrid optimal operation scheduling decision.