Hugo Miguel Inácio Pousinho

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.

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.

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.

Short-term wind power forecasting using a hybrid evolutionary intelligent approach

This paper presents a hybrid evolutionary intelligent approach, based on a combination of evolutionary particle swarm optimization (EPSO) with an adaptive-network-based fuzzy inference system (ANFIS), for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses challenges due to its intermittency and volatility. Hence, good forecasting tools are important for 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.

Mixed-Integer Nonlinear Programming Approach for Short-Term Hydro Scheduling

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.

Operations planning of a hydro producer acting as a price-maker in an electricity market

Renewable energy integration is gradually increasing in several European countries, such as Portugal and Spain, aiming to reduce emissions in power system operations. Particularly, significant investments in hydro energy systems are envisaged in Portugal, representing a secure and reliable energy source towards sustainable development. Additionally, a pool market structure implies that producers should aim at maximizing profits on a day-ahead basis. Usually, hydro power producers have been considered as price-takers, accepting the price signal as input data. Instead, a MILP approach is provided in this paper to determine the operations planning of a hydro producer acting as a price-maker in a day-ahead electricity market. A realistic case study is presented, based on a hydro energy system in cascaded configuration characterized by spatial-temporal interdependency between reservoirs, in order to thoroughly evaluate the expertise of the proposed approach.

Profit-based head-dependent short-term hydro scheduling considering risk constraints

This paper is on the short-term hydro scheduling (STHS) problem, particularly concerning a head-dependent cascaded hydro system. A mixed-integer quadratic programming approach is proposed for the STHS problem, considering not only head-dependency, but also discontinuous operating regions and discharge ramping constraints. Moreover, market uncertainty is introduced in the model via price scenarios, and risk-aversion is considered by limiting the volatility of the expected profit. Numerical results, based on one of the main Portuguese cascaded hydro systems, illustrate the proficiency of the proposed approach. Finally, conclusions are duly drawn.

Wind power short-term prediction by a hybrid PSO-ANFIS approach

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. A novel hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term wind power prediction. Results from a real-world case study are presented. Conclusions are duly drawn.