Planning for Mitigation of Variability in Renewable Energy Resources using Temporal Complementarity

Many regions in the world exhibit temporal complementarity of solar and wind energy. In such regions, during certain time periods, when more solar energy is present, lesser amount of wind energy is available and vice-verse. The motivation of this work is to develop a general planning methodology for the integration of these variable renewable energy (VRE) resources in smart power grids, while exploiting temporal complementarity to minimize the supply demand mismatch. We develop an appropriate analytical framework to determine the total investment needed in such VRE resources and the fraction of the total investment in each resource. A multivariate optimization problem is formulated and an optimal algorithm is developed to determine these parameters. To test the proposed methodology, a case study is developed for a region in Northern Ireland. In this region, based on the historical data of past 10 years and using daily average wind and solar capacity factors, we determine the Pearson correlation coefficient, which turns out to be −0.34, showing a sufficient degree of complementarity (anti-correlation) between solar and wind energy. Planning parameters are determined for different load profiles in our case study. General conclusion of the work is that once the temporal complementarity of solar and wind energy resources is exploited, the net supply variability is significantly reduced in microgrids. However, the total investment costs also increases, which is offset by the savings in the storage costs. In addition, reduced variability also leads to a reduction in storage losses, emissions losses, dispatch losses, fuel costs and network congestion.