• Energy Research

Abstract This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn.

This paper is on the problem of short-term hydro scheduling, particularly concerning a head-dependent hydro chain. We propose a novel mixed-integer nonlinear programming approach for optimizing power generation efficiency. The proposed approach considers not only the nonlinear dependence between power generation, water discharge and head, but also discontinuous operating regions, in order to obtain more realistic and feasible results. Numerical results, based on one of the Portuguese cascaded hydro systems, illustrate the proficiency of the proposed approach. Finally, conclusions are duly drawn.

This paper proposes evolutionary particle swarm optimization (EPSO) combined with an adaptive-network-based fuzzy inference system (ANFIS) for short-term electricity prices forecasting. In a deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. The accuracy of the price forecasting attained with the proposed intelligent system is thoroughly evaluated, reporting the numerical results from a real-world case study.

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn.

This paper is on the problem of short-term hydro scheduling, particularly concerning head-dependent cascaded hydro systems. We propose a novel mixed-integer quadratic programming approach, considering not only head-dependency, but also discontinuous operating regions and discharge ramping constraints. Thus, an enhanced short-term hydro scheduling is provided due to the more realistic modeling presented in this paper. Numerical results from two case studies, based on Portuguese cascaded hydro systems, illustrate the proficiency of the proposed approach.

This paper is on the problem of short-term hydro scheduling, particularly concerning a head-sensitive hydro chain. A novel mixed-integer nonlinear programming approach is proposed for optimizing power generation efficiency. The proposed approach considers not only the nonlinear dependence between power generation, water discharge and head, but also start-up costs for the hydro units and discontinuous operating regions, in order to obtain more realistic and feasible results. Numerical results based on one of the main Portuguese cascaded hydro systems illustrate the proficiency of the proposed approach.

AbstractThe increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro‐fuzzy inference approach is proposed for short‐term wind power forecasting. Results from a real‐world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

In a deregulated profit-based environment, consumers and producers need short-term intelligent prediction tools to predict their bid strategies in the energy market. Also, all markets players need accurate forecasting tools with lower uncertainty, allowing to maximizing their profits. Hence, this manuscript presents a new intelligent approach based on a combination of Wavelet Transform (WT), Evolutionary Particle Swarm Optimization (EPSO) and Adaptive Network and Fuzzy Inference System (ANFIS) for forecasting in power engineering systems. The results of two real-world case studies are shown, regarding energy prices and wind power, which show the proficiency of the proposed intelligent approach.