Evaluation of hydro-wind complementarity in the medium-term planning of electrical power systems by joint simulation of periodic stream fl ow and wind speed time series: A Brazilian case study

Wind variability and hydro-wind complementarity should be both considered in the planning of electrical power systems. In such cases, the spatio-temporal dependence between streamflow and wind speed regimes needs to be asses to balance the electricity generation. Medium-term hydrothermal scheduling problems (MTHS) are used to define operation policies for electrical power systems under 5-10 years horizon. MTHS uses stochastic optimization techniques fed by synthetic streamflow scenarios. To set better operation policies, such scenarios should well represent statistical features of historical data. With the rapid growth in the installed capacity of wind power, operators are encouraged to consider novel approaches to represent the dependence of hydrometeorological variables. This study integrates wind variability and hydro-wind complementarity in the medium-term planning of electrical power systems employing joint simulation of periodic streamflow and wind speed time series. The generated scenarios are used as input to derive monthly operational policies via Implicit Stochastic Optimization. A hydropower plant and a wind farm, both located in the Northeast region of Brazil were selected as a case study. Results show that considering wind variability and hydro-wind complementarity can significantly reduce energy deficits in power systems and increase the flexibility in the operation of water reservoirs. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Wind variability and hydro-wind complementarity should be considered in electrical power systems planning to balance electricity generation. Medium-term hydrothermal scheduling problems define operation policies for 5-10 years using stochastic optimization techniques and synthetic scenarios. Generated scenarios should accurately represent statistical features of historical data.