PhD Track Co-Supervision Research

Optimal and Degradation-Aware Siting and Sizing of Battery Energy Storage Systems in PV-rich Distribution Networks

Project Details

Abstract

With the growing  environmental concerns of air pollution and global warming as well as the advances in PV  technology and scalability of PV systems, there has been a huge increase in the penetration and usage of PV DGs in distribution networks, mainly in countries with high solar potential like  Lebanon. However, this outgrowing trend is impacting the safe and reliable operation of  distribution grids. Hence, the importance of developing methodologies to render the integration  of PV DGs into distribution networks more reliable and economically feasible. With the increase in the penetration of PV DGs in distribution networks, BESSs become a crucial  contributor to resolving the challenges pertaining to the unpredictable nature of PV energy. By  absorbing excess PV generation and supplying energy during hours of peak demand, BESSs  can enhance the voltage stability, reduce the power losses and increase the system’s reliability.  However, these benefits can only be realized if the BESSs are properly allocated in the network.  Hence, developing methodologies for the optimal siting and sizing of BESSs is essential to  maximize the benefits that they have on the distribution networks.  The strategic integration of the BESSs in distribution network as a solution measure to mitigate  the negative impacts emanating from the high penetration of PV DGs requires the determination  of several important parameters such as the locations and sizes of the BESSs, the configuration  to adopt (centralized vs. distributed), the BESS technology that would be best suited for this  type of application as well as the BESS model to consider. The objective of this research is to  develop a degradation-aware optimal siting and sizing methodology for BESSs in PV-rich  distribution networks. This methodology will rely on a multiple-stacked service optimization that accounts for technical, economic and environmental objectives. Furthermore, this multi-objective optimization will include BESS degradation models derived from experimental aging tests performed on several battery technologies.