A data-driven multi-objective optimization framework for optimal integration planning of solid-state transformer fed energy hub in a distribution network

Abstract

This study proposes an optimal planning technique for a solid-state transformer (SST) fed hybrid energy hub consisting of a wind turbine distributed generator (WTDG) and a battery energy storage system (BESS) in a distribution network (DN). The energy exchange within the energy hub and with the utility grid is regulated by an efficient energy management plan (EMP). The simultaneous planning of a WTDG-BESS energy hub in a DN fed through an SST is a challenging combinatorial optimization problem. It is not only about capacity allocation and placement, but also about charging and discharging dispatches of BESSs under dynamically altering system parameters with the intermittent nature of WTDG outputs and the operational limits of the system itself. As a result, simultaneous allocation becomes a mixed integer nonlinear programming (MINLP) problem, with the operational aspect acting as a limitation for the planning aspect and adding to the complexity of the problem. While the power flow takes into consideration the operational losses of the traditional distribution transformers (DT), the optimization problem reduces voltage deviations and energy losses of the DN. A 33-bus radial DN was used to test the proposed technique, and the outcomes of various case studies are included. The MINLP problem was addressed using the non-dominated sorted genetic algorithm (NSGA-II) in the MATLAB R2020a platform. The outcomes of the evaluation show an energy loss reduction (ELR) of 62%, while a voltage profile improvement (VPI) of 69% is obtained.