Hunan University

Jiayong Li

Cong Zhang

Lipeng Zhu

Bin Zhou

Zhikang Shuai

Weizhi Huang

Yifang Jin

Yingping Cao

Zhihao Hua

Quan Zhou

Limengqian Zheng

Ziqi Da

Yuhan Hu

Jie Hou

Weijia Wen

Mengwei Zhang

Hengxi Liu

Qian Liu

Huaizhi Yang

Bingxu Li

Dapeng Wang

The provided is a reactive power optimization method for a power system based on distributed interval optimization, including following steps: S. constructing a first model, where the first model includes an interval reactive power optimization model of a power system with a high renewable energy penetration rate, and the interval reactive power optimization model includes an objective function, a first constraint, and a constraint of an interval power flow equation; S. obtaining a first state variable interval based on the constraint of interval power flow equation and a distributed interval power flow algorithm; S. Converting the first model based on the first state variable interval and a security limit definition method, and obtaining a second model, where the second model includes a deterministic optimization model; and S. obtaining a voltage and reactive power control strategy based on the second model and a primal-dual interior point method.

Reactive power optimization method for power system based on distributed interval optimization, and computer medium

The provided is a generalized energy storage control method and system. The method includes: Acquiring a pre-established energy storage control model, where the energy storage control model includes an scheduling objective expression and power system constraints corresponding to the objective scheduling expression; Determining control parameters of the energy storage aggregation system based on the power system constraints and the objective scheduling expression; Determining an aggregated output curve of the energy storage aggregation system according to the control parameters, where the aggregated output curve characterizes the power output variation along time; Decomposing the aggregated output curve to obtain a respective decomposed output curve for each individual energy storage system within the energy storage aggregation system; Operating each energy storage system in accordance with its respective decomposed output curve. This method improves the operational security and reliability of the power system.

Generalized energy storage control method and system for full consumption of distributed renewable energy in distribution networks

A method for flexible coordinated operation of an urban distribution network and a watershed network, including: constructing a watershed network dynamic operation model; constructing a river water storage model a lake water storage model based on the watershed network dynamic operation model; constructing a distribution network linear alternating-current (AC) power flow model and an operating power-flow rate operation model of the pump stations to obtain a coordinated operation model of the urban distribution network and the watershed network; constructing a watershed-electricity composite sensitivity matrix; quantifying a time-varying adjustable power domain of each pump station through a pump station flexibility assessment method based on the watershed-electricity composite sensitivity matrix; constructing a power flow optimization model of the urban distribution network and a hydraulic energy flow optimization model of the watershed network; and performing electricity-water energy flow interactive optimization involving a flexible resource of the pump stations.

Method for flexible coordinated operation of urban distribution network and watershed network

A method and device for labeling transient voltage stability samples in a power grid based on semi-supervised learning are provided. The method includes: S: obtaining a transient voltage time series trajectory V formed for each load bus in the power grid under N transient operating scenarios; S: preliminarily labeling the stability status of each transient operating scenario with a voltage time series dataset V, and integrating the labeling result Yinto a class label dataset Y; S: constructing a voltage stability sample set S={(V, Y)|1≤i≤N}, dividing S into sample subsets Sand S; S: labeling samples in Sby using a semi-supervised clustering learning method and a semi-supervised classification learning method to obtain result datasets Yand Yrespectively; S: performing interactive verification on Yand Y, and updating Sand S; and S: performing repeated iteration on the Sand the S

Method and device for labeling transient voltage stability samples in power grid based on semi-supervised learning

A method for assessing short-term voltage stability of a power grid based on synchronous spatio-temporal information learning includes: performing a time-domain simulation for each transient fault of a power grid under each operating mode, and extracting temporal response trajectories of each monitoring bus and stability status information of the power grid to obtain a transient sample set; constructing a spatial adjacency matrix and a temporal adjacency matrix separately, and integrating the spatial adjacency matrix and the temporal adjacency matrix into a spatio-temporal adjacency matrix; performing synchronous spatio-temporal information learning by using a graph convolutional neural network algorithm, and performing training to obtain a short-term voltage stability assessment model driven by spatio-temporal information synchronization; and inputting a transient temporal responsive trajectory obtained into the short-term voltage stability assessment model, to obtain an assessment result of short-term voltage stability of the power grid.

Method for assessing short-term voltage stability of power grid based on synchronous spatio-temporal information learning

A method for electricity-related security awareness of distributed power supply systems considering spatio-temporal distribution of rainstorms, including: establishing a multi-dimensional parallel parasitic capacitance calculation model of the distributed photovoltaic-energy storage power supply system considering accumulated water depth and micro-terrain environment; performing multi-source spatio-temporal hierarchical correlation analysis between rainstorm spatio-temporal distribution characteristics (including rainfall peak position, cloud movement, rainfall intensity and rainfall duration) and an operating state of the distributed power supply system; constructing a leakage current probability prediction model considering unevenness and randomness of the rainstorm spatio-temporal distribution; and establishing an electricity-related security awareness model based on deep meta-learning.

Method for electricity-related security awareness of distributed power supply systems considering spatio-temporal distribution of rainstorms

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Changsha, China

Jiayong Li