In Brazil, the correct measurement of the individual firm energy of a plant is important, since it influences directly the determination of its assured energy which is used to establish contracts between power plants and distribution companies, free consumers, and traders. With increasing technological development and greater reliability in the use of automated techniques for monitoring, the use of the Acoustic Doppler Current Profiler (ADCP), has become a reality in Brazil. The ADCP has many advantages over the traditional techniques used for monitoring flows in gage stations of the national hydrometeorological network. In this context, the purpose of this work is to evaluate the impact of the streamflow rating curve measurement on the evaluation of the firm energy of a hydropower plant. A linear optimization model based on dynamic programming was used to calculate the firm energy and it was considered possible measurement errors in the plant’s inflow values and in the parameters of its polynomials that defines the upward and downward elevation. The results pointed that the two considerations had an impact on the calculated firm energy: the inflow measurements and the streamflow rating curve. Therefore, it is shown the importance of an accurate measurement of inflows for the evaluation of the plant’s firm energy.
This article presents a methodology for including wind power generation in the medium-term planning ofhydrothermal systems, where Stochastic Dual Dynamic Programming (SDDP) is widely applied in the literature to solve this class of problem. To assess the impact of the intermittent generation, wind power scenarios were generated through Weibull Distribution, which were applied to reduce the load, generating several demand scenarios. Thus, the aim of this paper is to improve the computational eort of the conventional SDDP with demand scenarios, where the main contribution of the work consists of applying the Immediate Cost Function (ICF) to accelerate the SDDP convergence process. The proposed methodology was analyzed using part of the Brazilian system, considering a wind farm.
The hydrothermal coordination problem of real systems has a great complexity. This complexity is linked to the need of model the hydrological uncertainties of the systems. For this purpose, a tree of possible inflow scenarios is built. In order to solve this problem several methodologies can be applied, including Dual Dynamic Programming (PDD). Although, for large problems, such as the Brazilian system, this methodology results in an elevated time of resolution. The work presents a methodology with the objective of reduce this time through the asynchronous parallelization of the PDD, together with the optimum grouping of the nodes of the scenario tree in subproblems by using a genetic algorithm. Resumo: O problema de planejamento energético de sistemas hidrotérmicos reais possui uma grande complexidade, a qual está ligadaà necessidade de se modelar as incertezas hidrológicas. Logo,é realizada a construção de umaárvore de possíveis cenários de afluênciasàs usinas hidrelétricas. Para a solução deste problema várias metodologias podem ser aplicadas, entre elas a Programação Dinâmica Dual (PDD), porém para problemas grandes, como o do sistema brasileiro, esta metodologia pode demandar um esforço computacional elevado. O trabalho apresenta uma metodologia com o objetivo de diminuir o tempo de processamento através da paralelização de forma assíncrona da PDD juntamente com o agrupamentoótimo dos nós dá arvore de cenários em subproblemas, por meio de algoritmo genético.
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