Electrifying the future: smart scheduling powers up EV stations for a greener grid

Research Background

As electric vehicles (EVs) zoom into the mainstream—thanks to pioneers like Tesla and a global push to ditch fossil fuels—the power grid is feeling the strain from surging charging demands. Traditional setups struggle with reliability, costs, and environmental impacts, especially when integrating intermittent solar photovoltaic (PV) systems. Enter the game-changer: EV charging stations (EVCS) fused with PV panels and battery energy storage systems (BESS). This research tackles the chaos by optimizing station placement and operations in distribution networks, ensuring efficient power flow while curbing emissions and expenses. By addressing these hurdles, it paves the way for sustainable transport that doesn't overload our aging grids, making EVs a practical choice for everyone from city commuters to long-haul drivers.

Results and Benefits

The study deploys a cutting-edge multi-objective remora optimization algorithm (MOROA) to pinpoint the best spots for two EVCS in a standard IEEE 33-bus radial distribution system, while fine-tuning PV and BESS capacities. Key outcomes? It slashes total power losses by optimizing energy flows, trims annual costs—including substation power bills and PV/BESS upkeep—through smart resource allocation, and cuts upstream grid emissions for a cleaner footprint. In four detailed case studies, MOROA outperformed traditional methods, reducing peak loads and voltage fluctuations even under varying EV demands.

A standout feature is the criteria weight ranking mechanism, which prioritizes EV charging requests based on owner preferences like arrival time, departure needs, and battery state-of-charge (SOC)—ensuring fair, efficient slot assignments. Socially, this means less grid strain, translating to fewer blackouts and lower electricity rates for communities. EV owners enjoy faster, cheaper charges, while station operators boost profits through optimized PV-BESS integration. Environmentally, minimized emissions support global carbon neutrality goals, potentially averting tons of CO2 annually in high-EV adoption areas. Overall, these results demonstrate how intelligent scheduling lightens the grid's burden, fostering economic savings and eco-friendly mobility for society at large.

Future Application Prospects

This MOROA-driven approach could revolutionize urban planning, embedding smart EVCS into smart cities where PV-BESS combos handle real-time demands from massive EV fleets. Imagine highways dotted with self-sustaining stations that store solar energy for night-time rushes, or residential hubs that double as grid stabilizers. Further research might incorporate AI for predictive EV traffic modeling or hybrid renewables like wind, enhancing resilience against weather variability. Practically, it could inspire policies for incentives on low-emission infrastructure, attracting investors and accelerating EV adoption. By refining uncertainties in EV behaviors—such as random arrivals—future iterations could optimize larger networks, like IEEE 69-bus systems, driving down costs and emissions even further for a seamless transition to electrified transport worldwide.

Conclusion

This pioneering work innovates by harmonizing EV scheduling with PV-BESS tech through MOROA, striking a vital balance between efficiency, economy, and ecology. Its contributions not only fortify power grids against the EV boom but also propel us toward a decarbonized future, where sustainable driving is the norm, empowering cleaner air and energy independence for generations ahead.

Reference

Author: Sigma Ray ^a, Kumari Kasturi ^a, Manas Ranjan Nayak ^b

Title of original paper:  Multi-objective electric vehicle charge scheduling for photovoltaic and battery energy storage based electric vehicle charging stations in distribution network

Article link: https://www.sciencedirect.com/science/article/pii/S2773153725000465

Journal: Green Energy and Intelligent Transportation

DOI: 10.1016/j.geits.2025.100296

Affiliations:

^a Dept. of Electrical Engg., Siksha ‘O' Anusandhan University, Bhubaneswar 751030, India

^b Dept. of Electrical Engg., Biju Patnaik University of Technology, Rourkela 769015, India