Víctor Manuel Fernandes Mendes

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal

The increased integration of wind power into the electric grid, as it occurs today in Portugal, poses new challenges due to its intermittency and volatility. Wind power forecasting plays a key role in tackling these challenges. A novel hybrid approach, combining wavelet transform, particle swarm optimization, and an adaptive-network-based fuzzy inference system, is proposed in this paper for short-term wind power forecasting in Portugal. A thorough comparison is carried out, taking into account the results obtained with seven other approaches. Finally, conclusions are duly drawn.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. 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. Finally, conclusions are duly drawn.

Scheduling of Head-Sensitive Cascaded Hydro Systems: A Nonlinear Approach

In this paper, we propose a novel nonlinear approach to solve the short-term hydro scheduling problem under deregulation, considering head-dependency. The actual size of hydro systems, the continuous reservoir dynamics and constraints, the hydraulic coupling of cascaded hydro systems, and the complexity associated with head-sensitive hydroelectric power generation still pose a real challenge to the modelers. These concerns are all accounted for in our approach. Results from a case study based on one of the main Portuguese cascaded hydro systems are presented, showing that the proposed nonlinear approach is proficient.

An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal

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, reporting the numerical results from a real-world case study.

Optimal Offering Strategies for Wind Power Producers Considering Uncertainty and Risk

This paper provides a two-stage stochastic programming approach for the development of optimal offering strategies for wind power producers. Uncertainty is related to electricity market prices and wind power production. A hybrid intelligent approach, combining wavelet transform, particle swarm optimization and adaptive-network-based fuzzy inference system, is used in this paper to generate plausible scenarios. Also, risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study, based on a wind farm in Portugal, are provided and analyzed. Finally, conclusions are duly drawn.

Lightning Data Observed With Lightning Location System in Portugal

This paper presents an investigation into cloud-to-ground lightning activity over the continental territory of Portugal with data collected by the National Lightning Location System. The lightning location system in Portugal is first presented. Analyses about geographical, seasonal, and polarity distribution of cloud-to-ground lightning activity and cumulative probability of peak current are carried out. An overall ground flash density map is constructed from the database, which contains the information of more than five years and almost four million records. This map is compared with the thunderstorm days map, produced by the Portuguese Institute of Meteorology, and with the orographic map of Portugal. Finally, conclusions are duly drawn.

An Artificial Neural Network Approach for Short-Term Electricity Prices Forecasting

This paper presents an artificial neural network approach for short-term electricity prices forecasting. In the new 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. A three-layered feedforward artificial neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting the next 168 hour electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed approach, reporting the numerical results from a real-world case study based on an electricity market.

Neural Networks and Wavelet Transform for Short-Term Electricity Prices Forecasting

This paper proposes neural networks in combination with wavelet transform for short-term electricity prices forecasting. In the new 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 approach is thoroughly evaluated, reporting the numerical results from a real-world case study based on the electricity market of mainland Spain.

Profit-Based Short-Term Hydro Scheduling considering Head-Dependent Power Generation

This paper is on the problem of short-term hydro scheduling, particularly concerning head-dependent reservoirs under competitive environment. We propose a method, based on nonlinear programming, for optimizing power generation efficiency. The proposed method considers not only that the hydroelectric power generation is a function of the water discharge and of the head, but also that the maximum power generation is head-dependent. Numerical results based on a realistic cascaded hydro system illustrate the proficiency of the proposed method.

Wind Turbines with Permanent Magnet Synchronous Generator and Full-Power Converters: Modelling, Control and Simulation

Research about dynamic models for grid-connected wind energy conversion systems is one of the challenges to achieve knowledge for the ongoing change due to the intensification of using wind energy in nowadays. This book chapter is an involvement on those models, but dealing with wind energy conversion systems consisting of wind turbines with permanent magnet synchronous generators (PMSG) and full-power converters. Particularly, the focus is on models integrating the dynamic of the system as much as potentially necessary in order to assert consequences on the operation of system. In modelling the energy captured from the wind by the blades, disturbance imposed by the asymmetry in the turbine, the vortex tower interaction, and the mechanical eigenswings in the blades are introduced in order to assert a more accurate behaviour of wind energy conversion systems. The conversion system dynamic comes up from modelling the dynamic behaviour due to the main subsystems of this system: the variable speed wind turbine, the mechanical drive train, and the PMSG and power electronic converters. The mechanical drive train dynamic is considered by three different model approaches, respectively, onemass, two-mass or three-mass model approaches in order to discuss which of the approaches are more appropriated in detaining the behaviour of the system. The power electronic converters are modelled for three different topologies, respectively, two-level, multilevel or matrix converters. The consideration of these topologies is in order to expose its particular behaviour and advantages in what regards the total harmonic distortion of the current injected in the electric network. The electric network is modelled by a circuit consisting in a series of a resistance and inductance with a voltage source, respectively, considering two hypotheses: without harmonic distortion or with distortion due to the third harmonic, in order to show the influence of this third harmonic in the converter output electric current. Two types of control strategies are considered in the dynamic models of this book chapter, respectively, through the use of classical control or fractional-order control. Case studies were written down in order to emphasize the ability of the models to simulate new contributions for studies on grid-connected wind energy conversion systems.