Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Entire DC Network
Uncertainty Management In Multiobjective Hydro-Thermal Self-Scheduling Under Emission Considerations, Jamshid Aghaei, Abdollah Ahmadi, Abdorreza Rabiee, Vassilios G. Agelidis, Kashem M. Muttaqi, H A. Shayanfar
Uncertainty Management In Multiobjective Hydro-Thermal Self-Scheduling Under Emission Considerations, Jamshid Aghaei, Abdollah Ahmadi, Abdorreza Rabiee, Vassilios G. Agelidis, Kashem M. Muttaqi, H A. Shayanfar
Faculty of Engineering and Information Sciences - Papers: Part A
In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units' contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte …
95% Prediction Regions: Multivariate Uncertainty Quantification For Retrieved Atmospheric States, Noel A. Cressie, Sandy Burden
95% Prediction Regions: Multivariate Uncertainty Quantification For Retrieved Atmospheric States, Noel A. Cressie, Sandy Burden
Faculty of Engineering and Information Sciences - Papers: Part A
Poster presentation
Dispatch Strategy To Minimise Uncertainty In Wind Power Generation In The Australian National Electricity Market, Amila Wickramasinghe, Lasantha G. Meegahapola, Ashish P. Agalgaonkar, Sarath Perera
Dispatch Strategy To Minimise Uncertainty In Wind Power Generation In The Australian National Electricity Market, Amila Wickramasinghe, Lasantha G. Meegahapola, Ashish P. Agalgaonkar, Sarath Perera
Faculty of Engineering and Information Sciences - Papers: Part A
2014 ACPE. With increased penetration of wind power, scheduling generators to meet the forecast demand of a power system is becoming an increasingly challenging task for the system operators. Uncertainty associated with the generation expected from wind plants adds to the load demand uncertainty, making it necessary to retain additional reserves to maintain the balance between demand and supply of power. In the Australian national electricity market (NEM), sophisticated wind forecasting techniques are employed to reduce the uncertainty in wind generation. Despite being able to project the contribution of wind power to a reasonable accuracy, wind power plants are currently …
An Mle Method For Finding Lkb Ntcp Model Parameters Using Monte Carlo Uncertainty Estimates, Martin Carolan, Bradley Oborn, Kerwyn Foo, Annette Haworth, Sarah Gulliford, Martin A. Ebert
An Mle Method For Finding Lkb Ntcp Model Parameters Using Monte Carlo Uncertainty Estimates, Martin Carolan, Bradley Oborn, Kerwyn Foo, Annette Haworth, Sarah Gulliford, Martin A. Ebert
Faculty of Engineering and Information Sciences - Papers: Part A
The aims of this work were to establish a program to fit NTCP models to clinical data with multiple toxicity endpoints, to test the method using a realistic test dataset, to compare three methods for estimating confidence intervals for the fitted parameters and to characterise the speed and performance of the program.
Liner Shipping Fleet Deployment With Cargo Transshipment And Demand Uncertainty, S Wang, Q Meng
Liner Shipping Fleet Deployment With Cargo Transshipment And Demand Uncertainty, S Wang, Q Meng
Faculty of Engineering and Information Sciences - Papers: Part A
This paper addresses a novel liner shipping fleet deployment problem characterized by cargo transshipment, multiple container routing options and uncertain demand, with the objective of maximizing the expected profit. This problem is formulated as a stochastic program and solved by the sample average approximation method. In this technique the objective function of the stochastic program is approximated by a sample average estimate derived from a random sample, and then the resulting deterministic program is solved. This process is repeated with different samples to obtain a good candidate solution along with the statistical estimate of its optimality gap. We apply the …