A Two-Stage Online Algorithm for EV Charging Station Energy Management and Carbon Trading

TL;DR

This paper introduces a two-stage online algorithm for EV charging station energy management and carbon trading, effectively balancing real-time EV power flexibility with carbon footprint constraints to optimize costs and emissions.

Contribution

It presents a novel two-stage online approach that jointly manages energy and carbon trading considering different time scales and dynamic carbon footprint modeling.

Findings

The algorithm ensures the carbon footprint stays within emission quotas.

It achieves near offline optimal operation costs.

Case studies demonstrate its effectiveness and advantages.

Abstract

The increasing electric vehicle (EV) adoption challenges the energy management of charging stations (CSs) due to the large number of EVs and the underlying uncertainties. Moreover, the carbon footprint of CSs is growing significantly due to the rising charging power demand. This makes it important for CSs to properly manage their energy usage and ensure their carbon footprint stay within their carbon emission quotas. This paper proposes a two-stage online algorithm for this purpose, considering the different time scales of energy management and carbon trading. In the first stage, the CS characterizes the real-time aggregate EV power flexibility, in terms of upper and lower bounds on the total charging power, by a Lyapunov optimization-based online algorithm. In the second stage, the CS co-optimizes energy management and carbon trading, with EV charging power chosen within the aggregate flexibility region provided by the first stage. A generalized battery model is proposed to capture the dynamic carbon footprint changes and carbon trading. A virtual carbon queue is designed to develop an online algorithm for the second stage, which can ensure the carbon footprint of CS be within its carbon emission quota and its total operation cost is nearly offline optimal. Case studies validate the effectiveness and advantages of the proposed algorithm.