Efficient Deployment of Electric Vehicle Charging Infrastructure: Simultaneous Optimization of Charging Station Placement and Charging Pile Assignment

TLDR

Charging infrastructure deployment involves optimally placing stations and piles under constraints such as demand and range, and is known to be NP‑complete. The paper proposes a multicriteria‑oriented approach to efficiently deploy charging infrastructure. The authors formulate five submodular objective functions, design accelerated greedy algorithms (LGDG and LGEG) with provable guarantees for CSPL, and employ an Erlang‑Loss model to optimally assign charging piles. Experiments on real datasets demonstrate that the approach outperforms state‑of‑the‑art methods in effectiveness and efficiency, offering a potent solution for large‑scale EV charging infrastructure planning.

Abstract

Charging infrastructure deployment is to seek the proper plan of settling charging stations and charging piles under multiple constraints, such as recharging demand, cruising range, etc., and it has been asserted as an NP-Complete problem. In this paper, we propose a multicriteria-oriented approach of efficiently deploying charging infrastructure to cope with the problem. We firstly formulate five realistic charging objective functions that exhibit a significant diminishing returns effect, i.e., submodularity, and then exploit the submodularity of these objectives to design the acceleration algorithms for Charging Station PLacement (CSPL) with the provable performance guarantees. The corresponding algorithms are respectively named Lazy Greedy with Direct Gain (LGDG) and Lazy Greedy with Effective Gain (LGEG), and they scale well to the road networks of arbitrary size. Relying on the inference that the linear combination of submodular functions is still a submodular function, we treat CSPL as a multicriteria optimization problem that can be efficiently solved by the proposed algorithms. Moreover, we employ Erlang-Loss system to gain an optimal Charging Pile ASsignment (CPAS), which is capable of reducing the gap between the growing complexity of charging demands and the constrained supply of charging resources in considering the correlation between the primary human activities and the charging process. The experimental evaluation with real data sets shows that, compared with the state-of-the-art methods, the proposed approach reveals better effectiveness and efficiency, and it offers a potent solution to the planning of charging infrastructure for electric vehicles with large-scale datasets in reality.