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Publications

Publications by Jean Sumaili

2011

Unit Commitment and Operating Reserves with Probabilistic Wind Power Forecasts

Authors
Audun Botterud; Jianhui Wang; Jorge Valenzuela; Ricardo Jorge Bessa; Hrvoje Keko; Jean Sumaili; Vladimiro Miranda

Publication
PowerTech2011 - IEEE PowerTech 2011, Trondheim, Norway

Abstract

2011

Unit Commitment and Operating Reserves with Probabilistic Wind Power Forecasts

Authors
A. Botterud; Z. Zhou; J. Wang; J. Valenzuela; Jean Sumaili; Ricardo Jorge Bessa; Hrvoje Keko; Vladimiro Miranda

Publication
PowerTech2011 - IEEE PowerTech 2011, Trondheim, Norway

Abstract
In this paper we discuss how probabilistic wind power forecasts can serve as an important tool to efficiently address wind power uncertainty in power system operations. We compare different probabilistic forecasting and scenario reduction methods, and test the resulting forecasts on a stochastic unit commitment model. The results are compared to deterministic unit commitment, where dynamic operating reserve requirements can also be derived from the probabilistic forecasts. In both cases, the use of probabilistic forecasts contributes to improve the system performance in terms of cost and reliability.

2011

Application of Probabilistic Wind Power Forecasting in Electricity Markets

Authors
Audun Botterud; Zhi Zhou; Jianhui Wang; Ricardo Jorge Bessa; Hrvoje Keko; Jean Sumaili; Vladimiro Miranda

Publication
Windpower2011 - Windpower 2011 Conference & Exhibition, Anaheim, USA

Abstract

2011

CLUSTERING-BASED WIND POWER SCENARIO REDUCTION TECHNIQUE

Authors
Jean Sumaili; Hrvoje Keko; Vladimiro Miranda; A. Botterud; J. Wang

Publication
PSCC'11 - 17th Power Systems Computation Conference , Stockholm, Sweden

Abstract
This paper describes a new technique aimed at representing wind power forecasting uncertainty by a set of discrete scenarios able to characterize the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative or focal scenarios associated with a probability of occurrence is created using clustering techniques. The advantage is that this allows reducing the computational burden in stochastic models that require scenario representation. The validity of the reduction methodology has been tested in a simplified Unit Commitment (UC) problem.

2011

Finding Representative Wind Power Scenarios and their Probabilities for Stochastic Models

Authors
Jean Sumaili; Hrvoje Keko; Vladimiro Miranda;

Publication
ISAP 2011 - 16th International Conference on Intelligent System Applications to Power Systems, Hersonissos, Grécia

Abstract
This paper analyzes the application of clustering techniques for wind power scenario reduction. The results have shown the unimodal structure of the scenario generated under a Monte Carlo process. The unimodal structure has been confirmed by the modes found by the information theoretic learning mean shift algorithm. The paper also presents a new technique able to represent the wind power forecasting uncertainty by a set of representative scenarios capable of characterizing the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative scenarios associated with a probability of occurrence can be created finding the areas of high probability density. This will allow the reduction of the computational burden in stochastic models that require scenario representation.

2010

Modeling and Experimental Determination of the Circuit Parameters of Thin Film PV Modules/Arrays

Authors
Filippo Spertino; Jean Sumaili; Horia Andrei; Adrian-Valentin Boicea; Gianfranco Chicco

Publication
WESC 2010 - 8th World Energy System Conference, vol.3, no.14, pp.132-139, Targoviste, Romania

Abstract
Nowadays, photovoltaic (PV) systems are more and more widely diffuse in the energy networks, with growing size of the installations. For relatively large sizes, the effect of parasitic parameters (capacitive and inductive) becomes more relevant. Thus, it becomes interesting to characterize the effects of these parameters on the PV system operation and to include these effects in the PV system models. In the classical model of PV cells or modules, based on single exponential or double exponential representations, the parasitic parameters are ignored. In order to include the effects of these parameters, it is possible to elaborate the experimental results obtained by the transient charge of an external capacitor connected to the PV generator terminals. Proper selection of the external capacitor can lead to highlight the effects of these oscillations, making it possible to interpret the behaviour of a PV module or array through suitable analytical models. Refined assessment of the parameters is then possible by synthesizing the experimental results of multiple executions of the transient charge of the capacitor through minimum error-based computational procedures. In this paper, a model containing the classical and parasitic parameters is constructed on the basis of the results obtained by fast sampling of the voltage and current waveforms gathered during the transient charge of an external capacitor. The assessment is carried out with particular reference to thin film technol

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