Voltage Stability Evaluation Incorporating Wind Power Intermittency

A simulation framework is proposed to evaluate the voltage stability of power systems incorporating wind power intermittency.Firstly，the power output modelings of three types of wind turbines are discussed.Secondly，the Jensen model is employed to simulate the wind farm with the wake effect.The Monte Carlo based technique is used to conduct the voltage stability evaluation incorporating the randomness of the wind speed based on the Weibull probability distribution.Thirdly，the relative sensitivity index（RSI）is calculated to identify weak buses during analysis.Finally，case studies with different simulation scenarios are carried out.Some statistical results involving weakness probability，expected value and variance of RSI as well as preliminary conclusions are drawn based on numerical simulation results.

Impact of Wind Farms of DFIG Type on Power System Transient Stability

The impact of large-scale grid-connected wind farms of Doubly-fed Induction Generator (DFIG) type on power system transient stability is elaborately discussed in this paper. In accordance with an equivalent generator/converter model, the comprehensive numerical simulations with multiple wind farms of DFIG type involved are carried out to reveal the impact of wind farm on dynamic behavior of existing interconnected power system. Different load models involving nonlinear load model and induction motor model are considered during simulations. Finally, some preliminary conclusions are summarized and discussed.

Convexification Technique for Optimal Thermal-Wind-Photovoltaic Coordination Dispatch Incorporating an Energy Storage System

This paper proposes a solution to implementing acoordinated optimal day-ahead dispatch in a hybrid thermalwind-photovoltaic power system incorporating an energy storagesystem (ESS). Our aim is to minimize total generation costand restrain the frequent change of ESS charging/dischargingstatus while meeting a series of system operating constraints,including a proposed coordinated dispatch strategy for thepurpose of reducing thermal power fluctuations. A novel twostage convexification technique (TSCT) is designed and leveragedto convert the original non-convex optimal day-ahead dispatchmodel, without taking into account the constraints of the proposed coordinated dispatch strategy into two convex quadraticprogramming problems. When introducing the constraint ofthe coordinated dispatch strategy, the corresponding inequalityconstraints are transformed into a series of linear equalityconstraints, after which the original optimal day-ahead dispatchmodel can be solved by the TSCT mentioned above. Finally,numerical simulations and comparative analysis are performedon the IEEE standard test systems to verify the validity andeffectiveness of the proposed model and method.

Very Short-term Spatial and Temporal Wind Power Forecasting: A Deep Learning Approach

In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data.However,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms.Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory performance.In this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is developed.Due to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively extracted.Furthermore,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real scenarios.Graphics processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time application.Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.

An Optimal Over-frequency Generator Tripping Strategy for Regional Power Grid with High Penetration Level of Renewable Energy

This paper proposes an optimal over-frequency generator tripping strategy aiming at implementing the least amount of generator tripping for the regional power grid with high penetration level of wind/photovoltaic(PV),to handle the over-frequency problem in the sending-end power grid under large disturbances.A steady-state frequency abnormal index is defined to measure the degrees of generator over-tripping and under-tripping,and a transient frequency abnormal index is presented to assess the system abnormal frequency effect during the transient process,which reflects the frequency security margin during the generator tripping process.The scenariobased analysis method combined with the non-parametric kernel density estimation method is applied to model the uncertainty of the outgoing power caused by the stochastic fluctuations of wind/PV power and loads.Furthermore,an improved fireworks algorithm is utilized for the solution of the proposed optimization model.Finally,the simulations are performed on a real-sized regional power grid in Southern China to verify the effectiveness and adaptability of the proposed model and method.

An enhanced cascading failure model integrating data mining technique

An enhanced cascading failure model integrating data mining technique is proposed in this paper.In order to better simulate the process of cascading failure propagation and further analyze the relationship between failure chains,in view of a basic framework of cascading failure described in this paper,some significant improvements in emerging prevention and control measures,the subsequent failure search strategy as well as the statistical analysis for the failure chains are made elaborately.Especially,a sequential pattern mining model is employed to find out the association pertinent to the obtained failure chains.In addition,a cluster analysis model is applied to evaluate the relationship between the intermediate data and the consequence of obtained failure chain,which can provide the prediction in potential propagation path of cascading failure to reduce the risk of catastrophic events.Finally,the case studies are conducted on the IEEE 10-machine-39-bus test system as benchmark to demonstrate the validity and effectiveness of the proposed enhanced cascading failure model.Some preliminary concluding remarks and comments are drawn.