An integrated decision-making approach for managing transformer tap changer operation while optimizing renewable energy storage allocation using ANP-entropy and TOPSIS

The power industry is currently undergoing a rapid transformation toward the maximum utilization of renewable energy resources. In grid-connected renewable energy systems, enhancing the voltage stability during the fluctuations in renewable energy outputs can be achieved using a transformer with built-in on-load tap changing. It is one of the main traditional voltage regulation techniques utilized to improve the voltage profile in the power grid. Due to the unpredictable nature of renewable energy penetration, the frequent operation of an on-load tap changer (OLTC) may increase significantly. Despite the proposed methods used to optimize the location and size of renewable generation, there is a lack of empirical or detailed studies that have been looking at the issue of tap changers, mainly the impact of renewable energy storage allocation. The main aim of this paper is to find a compromise approach to mitigate unnecessary tap-changing operation time by wisely siting and sizing the renewable energy storage system (RESS). Initially, a generalized method is proposed to formulate an analytical optimization framework coupled with a renewable storage index. This approach is employed to evaluate the behavior of OLTC in a grid-connected renewable energy system. The formulation is then used to model a decision-making approach to confirm the location and size of RESS, using the analytic network process (ANP) with entropy and technique for order of preference by similarity to an ideal solution (TOPSIS). The performance of the proposed approach is evaluated and compared using the IEEE benchmark networks. Aspects in relation to the predictions of future operations about the management of renewable energy storage in coordination with voltage regulators in the smart grid context, that will be the subject matter of additional studies are also discussed.