Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Publicações

Publicações por CPES

2013

A multi-scale optimization model to assess the benefits of a smart charging policy for electrical vehicles

Autores
Chammas, M; Chiche, A; Fournie, L; Nuno Fidalgo, JN; Couto, MJ;

Publicação
2013 IEEE GRENOBLE POWERTECH (POWERTECH)

Abstract
The recent development of electric vehicles (EVs) has brought a new set of problems regarding their integration in power networks, particularly in terms of the potential growth of peak load. The peak growth leads to the increase of losses and braches charging and to voltage drops. Conversely, optimizing EV charging policy creates new opportunities for both network safety and energy trading through the markets. This paper presents a multi-level framework combining two representations of a medium voltage (MV) network in order to optimize the EV charging policy. A minimizing cost approach is set, modeling day-ahead markets, and taking into account losses. The proposed methodology is tested on a typical MV network.

2013

Application of probabilistic wind power forecasting in electricity markets

Autores
Zhou, Z; Botterud, A; Wang, J; Bessa, RJ; Keko, H; Sumaili, J; Miranda, V;

Publicação
WIND ENERGY

Abstract
This paper discusses the potential use of probabilistic wind power forecasting in electricity markets, with focus on the scheduling and dispatch decisions of the system operator. We apply probabilistic kernel density forecasting with a quantile-copula estimator to forecast the probability density function, from which forecasting quantiles and scenarios with temporal dependency of errors are derived. We show how the probabilistic forecasts can be used to schedule energy and operating reserves to accommodate the wind power forecast uncertainty. We simulate the operation of a two-settlement electricity market with clearing of day-ahead and real-time markets for energy and operating reserves. At the day-ahead stage, a deterministic point forecast is input to the commitment and dispatch procedure. Then a probabilistic forecast is used to adjust the commitment status of fast-starting units closer to real time, on the basis of either dynamic operating reserves or stochastic unit commitment. Finally, the real-time dispatch is based on the realized availability of wind power. To evaluate the model in a large-scale real-world setting, we take the power system in Illinois as a test case and compare different scheduling strategies. The results show better performance for dynamic compared with fixed operating reserve requirements. Furthermore, although there are differences in the detailed dispatch results, dynamic operating reserves and stochastic unit commitment give similar results in terms of cost. Overall, we find that probabilistic forecasts can contribute to improve the performance of the power system, both in terms of cost and reliability. Copyright (c) 2012 John Wiley & Sons, Ltd.

2013

Demand Dispatch and Probabilistic Wind Power Forecasting in Unit Commitment and Economic Dispatch: A Case Study of Illinois

Autores
Botterud, A; Zhou, Z; Wang, JH; Sumaili, J; Keko, H; Mendes, J; Bessa, RJ; Miranda, V;

Publicação
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract
In this paper, we analyze how demand dispatch combined with the use of probabilistic wind power forecasting can help accommodate large shares of wind power in electricity market operations. We model the operation of day-ahead and real-time electricity markets, which the system operator clears by centralized unit commitment and economic dispatch. We use probabilistic wind power forecasting to estimate dynamic operating reserve requirements, based on the level of uncertainty in the forecast. At the same time, we represent price responsive demand as a dispatchable resource, which adds flexibility in the system operation. In a case study of the power system in Illinois, we find that both demand dispatch and probabilistic wind power forecasting can contribute to efficient operation of electricity markets with large shares of wind power.

2013

Reliability Assessment Unit Commitment with Uncertain Wind Power

Autores
Wang, J; Valenzuela, J; Botterud, A; Keko, H; Bessa, R; Miranda, V;

Publicação
Handbook of Wind Power Systems - Energy Systems

Abstract

2013

Simplified Cross-Entropy Based Approach for Generating Capacity Reliability Assessment

Autores
Carvalho, LD; Gonzalez Fernandez, RA; Leite da Silva, AML; da Rosa, MA; Miranda, V;

Publicação
IEEE TRANSACTIONS ON POWER SYSTEMS

Abstract
This paper presents a new algorithm to estimate the optimal importance sampling (IS) probability distribution in generating capacity reliability (GCR) problems. The proposed approach results from a combination of the cross-entropy (CE) concepts with the standard analytical GCR assessment. A mathematical analysis of the CE equations is carried out to demonstrate that the optimal change of measure or distortion can be obtained by simply dividing the annualized GCR indices for two different configurations of the generating system. Under these hypotheses, a straightforward algorithm based on fast Fourier transform is proposed to systematically obtain the optimal distorted unavailabilities for all generating units in the system. The accuracy and computational performance of the proposed approach are compared with the standard CE optimization process using different generating systems. The IEEE-RTS 79, IEEE-RTS 96, and two configurations of the Brazilian South-Southeastern system are all used for this purpose.

2013

Towards an Auto-Associative Topology State Estimator

Autores
Krstulovic, J; Miranda, V; Simoes Costa, AJAS; Pereira, J;

Publicação
IEEE TRANSACTIONS ON POWER SYSTEMS

Abstract
This paper presents a model for breaker status identification and power system topology estimation based on a mosaic of local auto-associative neural networks. The approach extracts information from values of the analog electric variables and allows the recovery of missing sensor signals or the correction of erroneous data about breaker status. The results are confirmed by extensive tests conducted on an IEEE benchmark network.

  • 191
  • 317