Pré-despacho incorporando fontes de energia eólica, maremotriz e sistemas de armazenamento de energia

Abstract

Unit commitment is an important step in the planning of the operation, generally aiming to minimize the total cost of the operation during the scheduling horizon, respecting constraints such as spinning reserve limits, minimum on and off times, ramp limits of generators, the restrictions of the active power balance and the power limits of the generators at each instant. In addition, to reduce dependence on fossil fuels and greenhouse gas emissions, the integration of clean and renewable energy resources into electricity systems has attracted worldwide attention. Thus, this dissertation presents an unit commitment model under smart grids environment, in addition to a methodology based on dynamic forward programming with the aim of solving the problem formulated. In the unit commitment problem were considered intermittent renewable energy sources (RES), and energy storage systems (ESS). Emphasis was given on intermittent RES to wind and tidal energy farms and ESS to battery storage systems. However, due to uncertainty in wind forecasting (which will cause differences between predicted and measured values), a probabilistic model (based on the Weibull probability density function) for wind turbines is formulated, which penalizes through a cost the fact that the power available for generation by the wind farm is underestimated or overestimated. The inclusion of the tidal energy farm to the unit commitment is simpler because, although intermittent, it is a very predictable source of energy. Finally, in order to optimize the cost of the unit commitment, the battery energy storage system for energy storage is incorporated into the problem. The proposed methodology was implemented in MATLAB® computing environment and applied to two commonly used test systems (with four and ten thermal generating units, respectively). The results of the simulation demonstrate the applicability and good performance of the proposed method, as well as the advantages of using energy storage systems to aid in the demand shifting, peak demand shaving besides increasing the economy.