7674results about "Ac networks with different sources same frequency" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The invention discloses an intelligent power distribution layered self-healing fault isolation and power supply recovery method, which is suitable for realizing rapid fault isolation and power supply recovery of a non-fault area when a power distribution system has a fault. According to the method, a hierarchical control framework is adopted, and intelligent switch equipment on the lower layer is responsible for collecting the current and voltage states of a line in real time and immediately completing local identification and fault section removal after a preset fault signal is detected; the fault information is then uploaded to a power distribution automation master station on the upper layer, the master station analyzes a recoverable power supply area in combination with the current network topology and generates a network reconstruction scheme, and a non-fault area is switched to a standby power supply path by controlling an interconnection switch; the system supports the backtracking analysis of historical data such as waveform distortion and partial discharge signals before a fault, identifies a potential abnormal trend, optimizes an operation strategy, and achieves a self-adaptive early warning function. The method has the advantages of being rapid in response, clear in structure and high in autonomy, and the reliability and continuity of the power distribution system can be remarkably improved.

A power grid planning method and system adapted to multiple uncertainties and multi-objective requirements, which relate to the technical field of power grid planning. The method comprises: constructing a deterministic multi-objective coordinated planning model with the objectives of minimizing economic cost, reliability risks and environmental impact; on the basis of an IGDT, constructing a multi-uncertainty model considering wind power, photovoltaic power and load; converting a model into a second-order cone programming model by using variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation methods; and solving the second-order cone programming model, in order to obtain a power grid planning scheme that adapts to uncertainties in wind and photovoltaic power output and load demand growth and meets the requirements for the economy, reliability and environmental friendliness of power grid operation. Thus, economic benefits of power grid planning are improved, carbon emissions from power grid operation are reduced, the reliability of a power grid is enhanced, and an obtained planning scheme has high robustness within expected objectives of planners.

The invention discloses a load flow calculation and simulation control method and system for a digital twin power grid, and relates to the technical field of digital twin simulation control, and the method comprises the following steps: constructing a digital twin power grid model, and carrying out the dynamic topology optimization processing of the digital twin power grid model based on remote signaling credibility weighting; according to the optimized digital twin power grid model, identifying the power grid operation risk based on an integrated learning model; according to the risk identification result, generating a transfer path control strategy based on an analytic hierarchy process and a fuzzy comprehensive evaluation method; mapping the transfer path control strategy into a control action instruction set, and simulating execution and establishing a feedback correction mechanism on the digital twin power grid model; by generating the optimal path control strategy and performing control strategy analog simulation and self-adaptive feedback correction based on the digital twin power grid model, the problem of lack of intelligent path control strategy selection and simulation verification based on state dynamic identification in the prior art is solved.

The invention discloses a multi-microgrid cooperative scheduling method and system based on the game theory, and the method comprises the steps: generating a dynamic game initial strategy set through a multi-agent strategy network according to the charge state of user energy storage equipment, the charge and discharge efficiency and the real-time scheduling demands of a power grid; based on the dynamic game initial strategy set, adopting an asymmetric Nash bargaining model to carry out distributed negotiation, and generating a balanced benefit distribution scheme; according to the equilibrium benefit distribution scheme, iteratively correcting the energy storage priority index by using a time decay type reinforcement learning algorithm, and generating a dynamic bidding rule containing a supply and demand elastic coefficient and risk compensation; and based on a dynamic bidding rule, energy storage resources are allocated in real time through a decentralized gradient consensus mechanism, and a final collaborative scheduling instruction is generated and synchronized to each micro-grid terminal. According to the embodiment of the invention, the operation efficiency and the self-adaptive capability of the multi-microgrid system can be improved, and the requirements of a future intelligent power distribution network are met.

Disclosed in the present application are a power grid real-time scheduling optimization method and system, a computer device and a storage medium. The method comprises: acquiring power grid model parameters and power grid operation data; and according to the power grid model parameters and the power grid operation data, obtaining a power grid real-time scheduling adjustment strategy by means of a preset power grid real-time scheduling reinforcement learning training model. Massive operation data of a power grid and load flow calculation simulation technologies can be fused by means of reinforcement learning, and unlike a conventional algorithm, a complex and difficult-to-solve calculation model does not need to be established, so that rapid optimization adjustment of power grid real-time scheduling is achieved, the optimization adjustment cost is reduced, and the matching degree of power grid real-time scheduling and actual operation is improved. The problem of real-time scheduling optimization of a power grid is solved, and the defects of difficulty in modeling in consideration of uncertain factors and slow calculation for solving large-scale optimization in existing algorithms due to the characteristics of strong uncertainty, rapidly increasing control scale and the like of novel power systems are overcome.

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 provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

The invention discloses a wind-light-hydrogen multi-energy complementary system cooperative operation optimization and comprehensive benefit evaluation method, which relates to the technical field of electric energy storage, and comprises the following steps: constructing a hybrid power flow model containing electricity, hydrogen and heat coupling, taking whole life cycle cost and new energy consumption as double targets, introducing grid-connected and off-grid risk constraints, and optimizing equipment capacity configuration; a scheduling baseline and an intra-day dynamic correction strategy are formulated in the day-ahead stage, and hybrid energy storage cooperative control is combined to improve the response speed and flexibility of the system to wind and light fluctuation; a peak regulation and frequency modulation mode is switched according to the power grid state, a power response model is established, fluctuation is stabilized through hydrogen energy storage, the stability of the power grid is enhanced, and auxiliary service benefits are created; the performance of the system is evaluated from the three dimensions of efficiency, economy and interactivity, dynamic weight and Monte Carlo simulation optimization are utilized, system parameters are continuously optimized through a feedback mechanism, and the self-evolution ability is formed. The problem that system instability and economy are difficult to take into account due to wind and light fluctuation is solved.

The invention discloses a power distribution network power supply reliability evaluation method and system based on data analysis, and relates to the technical field of power distribution network detection, a meteorological station, a GIS and an equipment monitoring terminal collect temperature, rainfall, wind speed, load and fault record data, and after time sequence alignment and interpolation correction, a unified environment data set is generated to provide an accurate basis for model construction; secondly, using historical and online data, calling a nonlinear mapping function and an equipment function to train a meteorological-fault rate model, outputting the equipment fault probability in real time, and then calculating a system risk index and a line risk value in combination with confidence-weighted weather forecast and equipment load coupling operation to accurately identify a high-risk area; priority scheduling, load transfer and graded load shedding are carried out on the high-risk area, fault information is transmitted back, closed-loop control is achieved, and the operation efficiency is improved.

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.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.

The invention relates to the technical field of power distribution network operation management, and discloses a power distribution network capacity-increase-free access system based on dynamic coordination. The system comprises a multi-source data acquisition and fusion unit which acquires multi-source data and fuses the multi-source data to generate dynamic power grid state characteristics; the multi-time-scale collaborative optimization architecture unit is used for constructing a layered architecture to realize optimization of different time scales; the space-time load prediction unit is used for predicting load distribution based on the space-time diagram model; and the multi-target dynamic optimization solving unit is used for constructing a model to solve an optimal scheduling instruction. In addition, a layered communication protocol unit is arranged to guarantee communication, and a voltage stability cooperative control unit and a distributed energy storage cooperative control unit are arranged to improve the stability of the power grid. Through cooperation of multiple technologies, capacity-increase-free access of the power distribution network is realized, the operation efficiency is improved, the loss is reduced, the voltage stability is ensured, and challenges brought by distributed energy and electric vehicle charging are effectively dealt with.

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.

A distributed photovoltaic consumption-oriented energy storage planning method and system for a flexible distribution network. The method comprises: acquiring grid data, photovoltaic data and load data of a distribution network in a target area, preprocessing the data, screening the preprocessed data, and selecting fault data; on the basis of the selected fault data and a genetic algorithm, establishing a distribution network energy storage system capacity configuration model; and on the basis of the distribution network energy storage system capacity configuration model, performing distributed photovoltaic consumption-oriented flexible distribution network energy storage planning.

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 intelligent power grids, and provides an intelligent operation centralized control center intelligent scheduling interaction method and system, and the method comprises the steps: generating a heat distribution gradient through environment noise filtering, reflecting the temperature field change of equipment, obtaining a voltage amplitude envelope through fundamental wave and harmonic wave separation, and monitoring the power quality; a current fluctuation coefficient is extracted by combining segmented deviation analysis to evaluate a line load state, and frequency fluctuation parameters are captured by using time domain statistics to track system stability. By constructing a dynamic association map, heterogeneous data such as thermodynamics, electrical quantity, load characteristics and synchronous states are mapped to power grid topological nodes, and a cross-dimension association relationship is established to realize whole-network state coupling analysis. An overload risk, a voltage out-of-limit probability and a system instability threshold are quantified through a hierarchical evaluation mechanism, a multi-target optimization constraint condition is formed, and finally a scheduling strategy considering equipment safety, economy and power supply reliability is generated. When the power system operates, intelligent scheduling is carried out according to the operation state of the power grid.

The invention relates to a follow-up network type control smooth switching method based on a generalized operation short-circuit ratio index. The follow-up network type control smooth switching method comprises the following steps of 1, introducing a generalized operation short-circuit ratio (GOSCR) as a switching index, and proposing a GOSCR-based hysteresis control mode switching judgment index under different power grid strength and power output working conditions; step 2, establishing a unified new energy grid-connected control unit model: constructing a unified grid-connected control model in two modes of network following control and network construction control, systematically analyzing oscillation characteristics of a grid-connected unit in the two modes, and defining boundary conditions of mode switching; 3, proposing an LADRC-based undisturbed switching control strategy: observing and estimating system current disturbance parameters online by utilizing LESO, realizing real-time compensation of disturbance quantity, and reducing dynamic sudden change in the switching process; the method has the advantages that GOSCR switching indexes are introduced to comprehensively evaluate the strength, a unified grid-connected control model is established, and an undisturbed switching control strategy is provided.

The invention provides a power system optimization control method and device based on power market transaction. The method comprises the following steps: acquiring real-time electricity price fluctuation data, load demand prediction data and dynamic topology connection data between power grid nodes; generating a simplified network structure adaptive to a transaction scene based on the data, and adjusting an equivalent impedance parameter of a key transmission path in real time; a node voltage amplitude and a phase angle are cooperatively regulated and controlled through a multi-stage regulation mechanism of the solid-state transformer, and a power distribution scheme is formed; monitoring operation data of the solid-state transformer in real time by utilizing an edge computing node, and generating a local control instruction by combining the current equivalent impedance parameter; and carrying out space-time mapping matching on the local instruction and the electricity price fluctuation data, generating a combined control strategy through collaborative priority ranking, and dynamically adjusting a power distribution scheme. According to the technical scheme provided by the invention, collaborative optimization of economical operation and physical transmission performance of the power system is realized.

The invention relates to the field of power systems, in particular to a power system fault prediction and self-healing control method and system. The method comprises the steps of monitoring electrical quantity of a power system in real time to obtain a monitoring data set; predicting potential fault points and fault types based on the monitoring data set; carrying out key monitoring on the area where the potential fault point is located; once abnormity or fault signs are found in key monitoring, fault diagnosis is carried out, the fault position is determined, according to a fault diagnosis report, a fault area is isolated, an instruction is generated and controlled, power supply of a non-fault area is automatically recovered, and a recovery operation log is recorded; a fault recovery strategy is automatically generated, and a control instruction is updated; and adjusting the operation of the power system according to the updated control instruction, and monitoring the operation state of the power system. According to the invention, the accuracy of fault prediction and the self-healing response speed are improved.

The invention discloses an intelligent regulation and control method and system for full-amount resource collaborative power grid power flow, and relates to the technical field of resource collaborative power grid power flow, and the method comprises the steps: obtaining a plurality of partition groups, and combining the partition groups with a power grid topology; obtaining a plurality of voltage grades, and randomly combining the partition groups with the voltage grades to obtain a plurality of efficiency optimization groups; voltage fluctuation data and power flow burden data in the power flow regulation and control process are obtained, and a power flow evaluation model is constructed based on a long-short-term neural network; and matching and constructing a point cloud grid model according to the output of the power flow evaluation model and the efficiency optimization group, performing surface analysis on the point cloud grid model to obtain the selected efficiency optimization group, and applying the selected efficiency optimization group to power grid power flow regulation and control, thereby solving the problems of contradiction between power grid power flow calculation precision and regulation and control efficiency and insufficient dynamic adaptability.

The invention discloses an adjustable load security access method for a virtual power plant, and relates to the technical field of power system automation, and the method comprises the following steps: in the operation process of a virtual power plant scheduling system, after an upper-layer scheduling center issues a unified adjustment instruction according to an operation demand, all access nodes are monitored in real time, and the adjustment instruction is sent to the upper-layer scheduling center; the method comprises the following steps: receiving a scheduling instruction from an access node, collecting response behavior data of the access node to the scheduling instruction, preprocessing the collected original response data of a single node, and carrying out unified coding and formatted storage on historical and current response behaviors according to node identification and scheduling time sequence information to construct a structured data set. According to the method, accurate identification and dynamic correction of the adjustable load frequency response offset risk in the virtual power plant are realized, the heterogeneous load perception and control capability is improved, frequency staggering resonance and scheduling failure caused by inconsistent response are avoided, the system cooperative adjustment stability and the load safety access reliability are enhanced, and the method has a good application prospect.

Disclosed in the present invention are a collaborative optimization method and system for multi-region energy storage and thermal power participation adequacy preventive control. The method comprises: setting an energy-storage total target outflow electricity quantity of each region; generating a feasible path library of an energy-storage charging and discharging policy of each region; selecting a reference path of each region, and constructing an energy-storage path set; under a given energy-storage path set, optimizing a global thermal power startup and shutdown policy; optimizing the energy-storage charging and discharging policy of each region without changing policies of other regions; using an optimal path of each region to update the energy-storage path set; determining whether the energy-storage path set or the number of iterations meets a preset convergence condition; if the energy-storage path set or the number of iterations meets the preset convergence condition, proceeding to the next step, otherwise, continuing to optimize the global thermal power startup and shutdown policy; and optimizing and outputting an energy-storage charging and discharging and thermal power startup and shutdown policy. The present invention can take power-electricity quantity coupling characteristics and startup and shutdown characteristics into comprehensive consideration to realize collaborative optimization of an energy-storage scheduling plan and a thermal power startup and shutdown plan within a given time scale, thereby effectively preventing and controlling operation risks of a power grid, and reducing scheduling control costs.

The invention discloses a distributed energy collaborative scheduling optimization method based on edge computing. According to the method, a plurality of edge computing nodes are deployed in a distributed energy system, a multi-protocol compatible OPC UA communication channel is constructed through protocol conversion middleware to collect data, and after the edge computing nodes clean and normalize the data, a preliminary scheduling scheme is generated through an improved genetic algorithm; the improved genetic algorithm is optimized through cooperation of a deep reinforcement learning model and an adaptive attenuation mechanism. And uploading the preliminary scheduling scheme to a cloud end, and obtaining a global optimal scheduling strategy through a multi-target particle swarm optimization algorithm. And the cloud carries out credible evidence storage on the global optimal scheduling strategy abstract value through an alliance chain smart contract, and establishes a PoA consensus mechanism. And when the communication is interrupted, the edge computing node starts the local emergency scheduling module, and incremental data synchronization is performed after the communication is recovered. The distributed energy scheduling optimization problem is effectively solved, the energy utilization efficiency is improved, and the system stability and reliability are enhanced.

The invention discloses a multi-region collaborative power grid planning system and method based on an improved multi-target particle swarm optimization algorithm, relates to the technical field of power system planning, and solves the problems of multi-target coupling and cross-region coordination in traditional power grid planning by constructing an economical, environment-friendly and reliable multi-dimensional target function and introducing a game theory method to quantify a multi-target constraint relation. The system comprises a data acquisition module, a multi-objective optimization model construction module, an improved particle swarm algorithm execution module, a collaborative decision module and a result output module, the improved particle swarm algorithm adopts dynamic adaptive inertia weight, time-varying acceleration coefficient and differential mutation operation, and the convergence speed and Pareto frontier distribution quality are remarkably improved; and the collaborative decision-making module realizes cross-regional parameter interaction and scheme optimization through a hierarchical collaborative mechanism and a fuzzy entropy theory. According to the method, collaborative optimization of calculation efficiency and scheme balance is realized in multi-regional power grid collaborative planning, and technical support is provided for scientific planning of a complex power grid system.

The invention discloses a line loss treatment method based on low-voltage power grid multi-source data fusion, and belongs to the technical field of information, and the method comprises the steps: reconstructing a power supply path based on a power grid topological structure and a changed power access point; extracting reverse power flow characteristics of the frequency points for correcting a node impedance matrix of the current power supply path to obtain a power supply access point impedance parameter set; updating a power transmission path through the reverse power flow characteristic to obtain a power distribution characteristic; and a power grid topological structure, a current power supply path, a power supply access point impedance parameter set and power distribution characteristics are fused to construct a line loss distribution prediction model and identify a line loss abnormal region, and the line loss abnormal region is treated. Through implementation of the method and the device, the problem that the line loss treatment effect is poor due to the fact that the influence of factors such as transformer area topological structure change, power access point change and load fluctuation on the line loss is not considered in the prior art, and the predicted line loss distribution has a large error compared with the actual situation can be solved.

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 present application discloses a calculation method for multi-scale electric-carbon energy efficiency optimization for a power system, which includes designing a carbon flow calculation improvement model of the power system that takes into account a photovoltaic cluster auxiliary service, converting a power lossy transmission network of the power system equivalent to a lossless power consumption network, and calculating a carbon flow of the power system that takes into account a photovoltaic reactive power cluster auxiliary service; designing a decision-making system for low-carbon operation of the power system based on a coupling of electric-carbon energy efficiency, and calculating a correlation between electric quantity and the carbon flow of the power system that takes into account the photovoltaic cluster auxiliary service; the output varies in the range of 0-1, larger values represent a stronger correlation between electric-carbon energy efficiency.

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.