Optimal Integration of Photovoltaic Public Charging Stations in Multi-Consumer Electrical Distribution System Using Improved Honey Badger Algorithm

Abstract

The penetration of electric vehicle (EV) loads in the electrical distribution system (EDS) is anticipated to increase dramatically over the next few years in response to rising global warming and fuel price scenarios. Planning for charging infrastructure and renewable energy sources (RES) is urgently required in this context. By carefully combining public charging stations (PCSs) backed by photovoltaic (PVs) systems and distribution static compensators (DSTATCOMs), this study proposes an optimisation strategy to reduce the adverse effects of EV load penetration in EDS. The proposed multi-objective function aims to reduce the voltage deviation index and distribution losses while considering various operational constraints. By combining the voltage stability index (VSI) and opposition-based learning (OBL) strategy with honey badger algorithm (HBA), the optimisation problem is resolved with a smaller search space for global minima. The effectiveness of the proposed technique on IEEE 33-bus EDS was evaluated in various situations. The real power losses are reduced to 242.35 kW, 75.32 kW and 41.76 kW from 350.25 kW by optimally integrating (i) PCSs alone, (ii) simultaneous PCSs and PVs and (iii) simultaneous PCSs, PVs and DSTATCOMs, respectively. Further, the VSI of the EDS is enhanced 0.7057, 0.8727, 0.9018 from 0.6104, respectively. The computational characteristics of HBA were also measured and compared with those of the COA and HBA. In terms of target and computational time, the outcomes produced by IHBA are very competitive with those of COA and superior to those of HBA. Additionally, even with a high EV load penetration, the suggested methodology produces reduced distribution losses and an appropriate voltage profile in EDS.

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

“…The heart-shaped updating process causes this local search capability to be underwhelming. The update mode of HBA (Thumati et al, 2023) that is modulated by a random parameter R, is thereby strengthened by the introduction of a spiral motion mechanism. If R < 0.3, it is updated near the current location.…”