Synergetic capacity planning of private and public EV charging piles via city-scale multiobjective optimization

TL;DR

This paper presents a city-scale, multiobjective optimization framework for EV charging infrastructure planning, addressing demand estimation, capacity allocation, and spatial distribution to support sustainable EV adoption.

Contribution

It introduces a demand-driven, spatially explicit optimization approach using Harris Hawks Algorithm for capacity planning, improving infrastructure deployment efficiency.

Findings

EV electricity consumption tripled by 2024, with seasonal volatility and a shift towards hybrid and extended-range EVs.

Optimized deployment outperformed actual infrastructure with a lower comprehensive performance score, indicating better service balance.

By 2030, Chongqing needs about 1.8 million charging units to maintain a 9:1 private-public ratio, reducing disparities.

Abstract

Rapid electric vehicle (EV) expansion necessitates optimized charging infrastructure to bridge the persistent gaps between vehicle growth and charger availability. This study develops a demand-driven framework for city-scale EV charging demand assessment and charging pile capacity planning. It employs a bottom-up estimation approach to quantify electricity demand and a Harris Hawks Optimization algorithm to solve capacity planning challenges, capturing spatiotemporal demand variations across powertrain types and guiding allocation over 2022-2030 in Chongqing, China. The results show that (1) compared with June 2022, monthly EV electricity consumption tripled to 57.5 gigawatt-hours by the end of 2024, characterized by significant seasonal volatility and a structural shift in which the combined share of plug-in hybrid electric vehicles and extended-range electric vehicles reached 57.6%, necessitating a transition toward technology-specific infrastructure planning; (2) historical evaluations reveal a marked spatial mismatch, with actual deployment heavily concentrated in the urban core while public charging capacity consistently lagging behind demand, whereas the proposed optimized configuration achieved a superior comprehensive performance score of 0.28, compared to 0.65 for actual deployment, in balancing service adequacy across the "Core-Suburban-Exurban" hierarchy; and (3) by 2030, Chongqing is projected to require approximately 1.8 million charging units to sustain a stable 9:1 private-to-public ratio, a synergetic strategy expects to significantly mitigate urban-rural service disparities and enhance overall system resilience and grid compatibility. Ultimately, this study provides a versatile, spatially explicit tool for policymakers to support sustainable and cost-effective EV infrastructure deployment aligned with long-term electrification targets.