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- Sistemas de decisão óptima em coordenação hidrotérmica para planeamento operacionalPublication . Mariano, Sílvio José Pinto SimõesA tese incide sobre o problema de afectação óptima de unidades e sobre os aspectos algorítmicos da sua solução, evoluindo no novo contexto da reestruturação do sector eléctrico. Para sistemas de energia eléctrica reais, o problema de afectação óptima de unidades assume grande dimensão e complexidade, que impossibilitam a sua resolução de forma directa, sendo aqui abordado recorrendo à relaxação Lagrangeana. A utilização da relaxação Lagrangeana permite resolver este problema de forma indirecta, exibindo contudo algumas dificuldades na obtenção de uma solução óptima e fazível é aqui conduzida de forma original uma análise ilustrada que evidencia quer as dificuldades deste problema ser abordado de forma directa, quer as limitações algorítmicas na obtenção da sua solução óptima utilizando relaxação Lagrangeana. Neste seguimento, é proposto um novo algoritmo que permite encontrar de forma automática a solução do problema relaxado. Evoluindo no novo contexto da reestruturação do sector eléctrico, aponta-se a estreita similaridade entre a interpretação económica destas técnicas de optimização e o mercado de energia eléctrica desregulado. Apresenta-se uma análise para diferentes cenários de mercado (regulado, desregulado e coexistência de ambos), verificando os seus comportamentos e reflectindo sobre a sua bondade. Por último, procurou-se satisfazer as exigências de optimização da exploração, no novo contexto da reestruturação do sector eléctrico, de novas empresas produtoras de energia eléctrica problema de optimização de uma central hidroeléctrica inserida num mercado desregulado.
- Short-term electricity prices forecasting in a competitive market: A neural network approachPublication . Catalão, J. P. S.; Mariano, S.; Mendes, V. M. F.; Ferreira, L. A. F. M.This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California.
- Parameterisation effect on the behaviour of a head-dependent hydro chain using a nonlinear modelPublication . Catalão, João Paulo da Silva; Mariano, S.; Mendes, V. M. F.; Ferreira, L. A. F. M.This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain. We use a method based on nonlinear programming (NLP), namely quadratic programming, to consider hydroelectric power generation a function of water discharge and of the head. The method has been applied successfully to solve a test case based on a realistic cascaded hydro system with a negligible computational time requirement and is also applied to show that the role played by reservoirs in the hydro chain do not depend only on their relative position. As a new contribution to earlier studies, which presented reservoir operation rules mainly for medium and long-term planning procedures, we show that the physical data defining hydro chain parameters used in the nonlinear model have an effect on the STHS, implying different optimal storage trajectories for the reservoirs accordingly not only with their position in the hydro chain but also with the new parameterisation defining the data for the hydro system. Moreover, considering head dependency in the hydroelectric power generation, usually neglected for hydro plants with a large storage capacity, provides a better short-term management of the conversion of the potential energy available in the reservoirs into electric energy, which represents a major advantage for the hydroelectric utilities in a competitive electricity market.