16 results about "Loading margin" patented technology

The invention discloses a new energy automobile dynamic charging power adjustment method considering a power grid load, and aims to solve the problems of power grid overload and non-fine regulation and control caused by concentrated charging of new energy automobiles. The method is executed by a regional charging coordination server and comprises the following steps: acquiring a real-time load value and a safety threshold value of a target line of the power distribution network; collecting real-time charging aggregation data of each charging station in the jurisdiction, wherein the real-time charging aggregation data comprises the power of each charging pile and the state of charge of a vehicle battery; calculating the load margin of the power grid and determining the total charging power needing to be adjusted; the charging urgency degree coefficient of each charging pile is calculated based on the state of charge of the vehicle battery, the total adjustment amount is distributed to each charging station according to the coefficient and the total power of the charging stations, and the initial power adjustment amount is generated; according to the invention, the real-time, collaborative and refined regulation and control of the charging load from the global perspective of the power grid is realized, under the premise of ensuring the safe operation of the power grid, the highly urgent charging demand is preferably met, and the resource allocation and user experience are optimized.

The embodiment of the invention provides a control method, device and equipment for low-voltage side switching equipment of a main transformer, a storage medium and a program product. The method comprises the following steps: acquiring a first current in a switch loop of first switch equipment; in response to the first current greater than or equal to the target safety current, determining at least partial load according to the first current and the target safety current; transferring at least part of the load of the first switching device to at least one second switching device, the second switching device having a load margin; the first switch device and the second switch device are power distribution control devices running in parallel on the low-voltage side of the main transformer; the target safety current is determined according to the historical measurement current of the switch loop and the historical measurement temperature under the historical measurement current. The method is used for improving the control accuracy of the low-voltage side switch equipment of the main transformer.

The invention relates to the field of accurate load shedding in a new energy area, in particular to an accurate load shedding method for a new energy area based on real-time measurement and data uploading. The method comprises the following steps: firstly, calculating a regional total shearable load based on interruptible load power; calculating the real-time load margin of the region based on the total shearable load of the region, the real-time output of the new energy and the fluctuation ratio of the output of the new energy; then, based on the calculated frequency deviation and the real-time load margin of each region, calculating a total load shedding demand; based on the total load shedding demand, the regional total shearable load and the real-time load margin, obtaining a distributed regional load shedding amount through a regional load optimal distribution algorithm; and finally, calculating to obtain a load shedding task of each load control terminal based on the distributed regional load shedding amount. The technical problems of non-uniform distribution of regional load shedding capacity, low efficiency, frequency deviation response lag and limitation of new energy output fluctuation on the load shedding capacity are solved.

The invention discloses an electric power system static voltage stability prevention and control method and device, terminal equipment and a storage medium, and belongs to the technical field of electric power system stability control, and the method comprises the steps: obtaining the current generator active output, terminal voltage, reactive compensation, line load, node voltage and node power of an electric power system; inputting the active output, the terminal voltage, the reactive compensation and the line load of the generator into a preset load margin prediction model constructed based on a convex neural network to obtain a first load margin prediction value; inputting the first load margin predicted value, the node voltage and the node power into a preset voltage stability control model to obtain a current control decision parameter of the power system; and finally performing voltage stability control on the power system according to the control decision parameter. By implementing the method, the problems of complex calculation and low solving efficiency of a method for formulating a control strategy by iteratively solving non-convex nonlinear constraints in the prior art can be solved.

The present invention belongs to the technical field of power grid voltage stability, and specifically relates to a method for rapid assessment of static voltage stability taking into account wind power prediction errors. In view of the shortcomings of the existing system voltage stability analysis methods in terms of low computational accuracy and efficiency, the present invention adopts the following technical solutions: historical data of wind farms are obtained, and a historical data set containing wind power predictions and measured power is constructed; the wind power prediction power in the historical data set is divided using a fixed step size to obtain different data subsets, and the wind power measured power at the same moment is divided into corresponding data subsets; the wind power prediction error in each data subset is calculated, and a non-parametric kernel density estimation method is used to construct a probability distribution of the power prediction error; a radial basis function proxy model is constructed to obtain a load margin, and a rapid assessment of the system static voltage stability is achieved based on the load margin. The beneficial effects of the present invention are: rapid and accurate assessment of static voltage stability is achieved.

The present disclosure relates to a wind turbine load shedding control method and device, the method comprising: obtaining operation data of each load channel of a wind turbine; generating a load margin corresponding to each load channel based on a measured load value and a load limit value in the operation data, determining a load channel to be shed and a corresponding load shedding demand from the load channel based on the load margin; calculating a residual execution capability and a channel credibility of each control channel of the wind turbine, and determining a set of available control channels based on the residual execution capability and the channel credibility; determining a control increment of a target control channel; and controlling the target control channel of the wind turbine based on the control increment to shed the load of the load channel to be shed. The risk of excessive power loss of the execution mechanism can be reduced, and the balance of the load shedding effect can be maintained by determining the set of available control channels and the corresponding control increment to allocate the load shedding demand, thereby improving the operation safety and load control stability of the wind turbine.

This application discloses a method, system, and medium for orderly charging scheduling of electric vehicles based on power nodes, relating to the fields of smart grid and charging facility management technology. The method includes: obtaining the real-time total load power of a substation, calculating the real-time load margin of the substation, and when a charging device requests to connect for charging, determining the target power of a first target charging device based on the real-time load margin of the substation and the requested access power of the charging device; when no charging device requests to connect for charging, but the real-time load margin of the substation is greater than or equal to a set threshold, and a charging device is in a power derating state, determining the target power of a second target charging device based on the real-time load margin of the substation, and subsequently issuing power scheduling instructions to complete orderly charging scheduling. This application can efficiently meet the growing charging demand while ensuring grid security.

A probabilistic transient voltage stability assessment method considering relevant random variables is proposed, comprising the following steps: Step 1: Modeling key random variables in the system, including determining their probability distribution types; Step 2: Calculating the maximum load margin under the current system configuration using the interior-point method, and using this margin as an index for evaluating the system's transient voltage stability; Step 3: Based on considering the random variables and their interrelationships, rationally selecting the power increment direction for generators and loads; Step 4: Generating samples by combining Latin hypercube sampling and singular value decomposition quadratic permutation techniques; Step 5: Conducting multiple simulation experiments using the generated sample set, and evaluating the probabilistic transient voltage stability through iterative deterministic calculations. This invention proposes a probabilistic transient voltage stability assessment method considering relevant random variables, preserving the actual operating characteristics of the power system.

The invention provides a transformer base installation stability and bearing capacity analysis method, which comprises the steps of performing fusion processing on a static stability index and a dynamic impact risk index to obtain a comprehensive risk coefficient, and combining the comprehensive risk coefficient with an oil liquid shaking period duration to determine an oil liquid shaking amplitude expansion amount under earthquake excitation; analyzing instantaneous dynamic load distribution of the base according to the peak value of the impact force in combination with the three-axis vibration acceleration signal of the base, identifying a load peak moment in the instantaneous dynamic load distribution of the base, and extracting a maximum impact load borne by the base corresponding to the load peak moment; the bearing margin of the base is evaluated according to the maximum impact load in combination with the rated bearing capacity of the base, and the dynamic bearing capacity grade of the base is determined according to the bearing margin.

The application discloses an eGBDT-based long-term voltage stability fine evaluation method and belongs to the field of power system operation and maintenance. The bus voltage amplitude and phase angle data of wide-area online measurement can be used to calculate a load margin index and realize fine classification of voltage stability. Meanwhile, the one-side gradient sampling method and the mutual exclusion feature bundling technology are adopted in the method to realize dimension reduction and feature compression of the high-dimensional measurement data space, effectively improving the processing efficiency of the traditional GBDT method on massive measurement data. The method inherits the high-precision estimation effect of the traditional GBDT method and greatly improves the processing capacity for massive measurement data. The application has stronger robustness for the uncertainty analysis caused by large-scale access of renewable energy sources and has good applicability when incomplete measurement data and different training data set sizes are processed. Therefore, the method has good practical application value.

The present application relates to photovoltaic power generation technical field, disclose a kind of photovoltaic power generation unit power generation stability monitoring control method and system, comprising: deployment voltage sensor in photovoltaic power generation unit key node, real-time acquisition voltage data;Establish dynamic voltage margin model, analyze voltage margin and calculate load margin index;Assess the stability of system under different load conditions;Voltage and frequency output by inverter are controlled using vector control algorithm.The present application is by deployment voltage sensor in photovoltaic power generation unit key node, real-time acquisition voltage data, establishes dynamic voltage margin model and calculates load margin index, can comprehensively analyze the voltage stability of system.By assessing the stability under different load conditions, ensure that system remains stability under complex operating conditions.Effectively improve the voltage control precision and dynamic response capability of photovoltaic power generation system.

The invention relates to the technical field of fault diagnosis, in particular to a power equipment fault diagnosis system and method, and the system comprises a multi-parameter mutation detection module, a heat-induced risk identification module, a load structure pre-judgment module, an abnormal behavior tracking module and a fault block defining module. According to the method, time gradient calculation and abrupt change direction consistency analysis are performed on transient change data of voltage, current and temperature, so that an operation time period with potential risks can be identified at the initial stage of linkage change of multiple parameters, and high-risk fragments caused by thermal response abnormity in a load structure are accurately captured; equipment nodes in a critical state are positioned on the basis of load disturbance behavior frequency and load margin trend, and a high-risk sequence with a synchronous evolution trend is extracted in combination with continuous parameter fluctuation consistency characteristics, so that dynamic identification of early symptoms of fault evolution and accurate demarcation of regional structure aggregation risks are realized. And the coverage capability and the troubleshooting efficiency of the diagnosis result on the fault area are improved.

The application belongs to the technical field of new energy, and particularly relates to a charging station low-power capacity orderly charging management system. The system comprises: a charging station orderly charging model; the charging station orderly charging model is configured to: receive a user input charging limit state and a chargeable waiting time; obtain N chargeable time periods of an electric vehicle of the user based on the chargeable waiting time; determine a required charging time period number of the electric vehicle based on the charging limit state and the N chargeable time periods; obtain a charging load margin of each chargeable time period; wherein the charging load margin is used to represent the power consumption load of the chargeable time period; and determine a charging strategy of the electric vehicle based on the charging load margin of each chargeable time period, the rated charging power of a charging pile of the charging station and the required charging time period number of the electric vehicle. In this way, power grid resources can be reasonably utilized, both the waste of power grid resources can be avoided and the load peak-valley difference can be reduced.

The application discloses an energy coordination scheduling method and system based on energy saving and carbon reduction, and relates to the field of energy coordination scheduling.The method comprises the following steps: collecting community power grid data and meteorological prediction data of a target community; calculating carbon emission intensity and load margin data of the target community, and planning an initial amount of charging rights for the target community; screening out high-carbon charging communities and low-carbon charging communities, and completing low-carbon electric energy scheduling; correcting the initial amount of charging rights in combination with the low-carbon electric energy scheduling result, obtaining an amount of charging rights, and generating a charging right bidding price of the target community; and completing optimal charging time period distribution of all charging pile users in the target community, and obtaining a charging scheduling scheme of the target community. The application can effectively solve the problem of unstable residential power consumption caused by concentrated charging.

The invention belongs to the technical field of data processing, and provides a load margin estimation method and system based on a mechanism enhanced neural network, and the method comprises the steps: extracting a key relation between a load power factor and a load margin from a voltage stability mechanism of a power system, and converting the key relation into a quantitative constraint formula; constructing a neural network model fusing physical mechanism constraints, and setting a composite loss function including empirical loss and mechanism loss according to a quantitative constraint formula; constructing a sample data set containing continuous load growth and emergencies, inputting the sample data set into the neural network model, and iteratively updating model parameters of the neural network model according to a composite loss function until training termination conditions are met, thereby obtaining a load margin estimation model; and obtaining actual measurement state data of the power system, and inputting the actual measurement state data into the load margin estimation model to obtain a load margin estimation value. According to the scheme, the generalization ability, the prediction accuracy and the physical consistency of the model are improved.

The invention belongs to the technical field of power optimization configuration, and provides an energy storage device power configuration method and system giving consideration to economy and voltage stability, and the method comprises the steps: determining an energy storage device needing to be configured according to the network loss sensitivity and the voltage sensitivity; establishing a multi-objective optimization model by taking the lowest total cost of energy storage equipment, the minimum system load margin difference value and the minimum static voltage stability L index as objectives; solving the multi-objective optimization model by adopting a multi-objective function solving optimization algorithm; according to the network loss sensitivity and the voltage sensitivity, a scientific and appropriate energy storage equipment position is selected, and the problems of voltage out-of-limit, network loss increase and the like caused by large-scale distributed wind and light power supplies and system loads can be efficiently solved; on the basis of considering economical efficiency and voltage stability targets, a multi-objective function solution optimization algorithm is adopted, and the problem that the weight of each sub-objective function is difficult to select is solved.