Statistical scenarios forecasting method for wind power ramp events using modified neural networks

Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years.Several forecasting techniques for wind power ramp events have been reported.In this paper,the statistical scenarios forecasting method is proposed for wind power ramp event probabilistic forecasting based on the probability generating model.Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics,respectively.Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions.A number of statistical scenarios captured bands are generated accordingly.Eventually,ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event forecasting method.A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis.It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions.Moreover,the statistical scenarios forecasting method can accurately estimate the characteristics of wind power ramp events.The results verify that the proposed method can guide the generation method of statistical scenarios and forecasting models for ramp events.

Analysis of factors influencing carbon emissions in the Yangtze River Delta region and projections of carbon peak scenarios

Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.

Multi-dimensional scenario forecast for generation of multiple wind farms

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

Agent-based modeling of COVID-19 outbreaks for New York state and UK:Parameter identification algorithm

This paper uses Covasim,an agent-based model(ABM)of COVID-19,to evaluate and scenarios of epidemic spread in New York State(USA)and the UK.Epidemiological parameters such as contagiousness(virus transmission rate),initial number of infected people,and probability of being tested depend on the region's demographic and geographical features,the containment measures introduced;they are calibrated to data about COVID-19 spread in the region of interest.At the first stage of our study,epidemiological data(numbers of people tested,diagnoses,critical cases,hospitalizations,and deaths)for each of the mentioned regions were analyzed.The data were characterized in terms of seasonality,stationarity,and dependency spaces,and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model.At the second stage,the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters.The model was validated with the historical data of 2020.The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved,the number of positive cases in New York State remain the same during March of 2021,while in the UK it will reduce.