Performance Assessment of Peak Shaving Techniques with Battery Energy Storage System in Small-scale Electrical Networks

Due to the overgrowing electrical demand in power systems, the electrical networks and end-users are facing numerous problems, including high demand charges, power losses across the electrical networks, voltage-frequency imbalance, etc. The inclusion of battery energy storage systems (BESS) with electrical networks for demand minimization is a suitable solution to overcome these concerns to ensure optimal and economic power flow. In this paper, two different peak load shaving techniques namely, the Fixed Demand Limit (FDL) technique and Decision Tree based Peak Shaving Algorithm (DT-PSA), are assessed to find the potentiality of peak shaving performance. Both techniques can reduce peak demands in different prospects as they have advantages and limitations in real scenarios. The aim of this study is to evaluate the operational performance of these two strategies on peak shaving applications, i.e., whether they can fit in small-scale electrical networks to ensure economic and optimal operation. Various time-varying load profiles are applied in real scenarios for both techniques to evaluate their peak shaving performances. Simulation results exhibit the functionality of the strategies.