Determining optimal spatial and temporal complementarity between wind and hydropower

The variable and uncertain nature of renewable energy power plants (REPP) generation can result in grid integration and energy imbalance problems. In this study, it was aimed to minimize the variability and uncertainty of REPP generation by considering spatial and temporal complementarity. In this context, a Critical Time Windows (CTW) based optimization model was developed to determine REPP clusters that have optimum complementarity index values. The Binary Artificial Bee Colony algorithm was employed in order to resolve the optimization model. The proposed method was applied to Wind Power Plants (WPP) and Run of River Hydropower Plants (RHPP) installed in Turkey. The proposed methodology was used in determining WPP, RHPP, and WPP+RHPP clusters with maximum spatial and temporal complementarity. It was seen that optimum solutions decreased the low generation frequencies by 9.6%, 21.75%, and 19.28% for WPP, RHPP, and WPP+RHPP clusters. This decrease in low generation frequency will reduce the thermal power backup capacity needed for the secure and economic operation of the grid. The study's results revealed that the proposed methodology is able to determine candidate REPP cluster regions for secure and economic capacity expansion plans. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study aimed to minimize the variability and uncertainty of renewable energy power plants (REPP) generation by considering spatial and temporal complementarity using a Critical Time Windows (CTW) based optimization model and a Binary Artificial Bee Colony algorithm. The proposed method successfully reduced the low generation frequencies of Wind Power Plants (WPP), Run of River Hydropower Plants (RHPP), and their combination, WPP+RHPP clusters, by significant percentages.