Three-Layer Joint Distributionally Robust Chance-Constrained Framework for Optimal Day-Ahead Scheduling of E-mobility Ecosystem

TL;DR

This paper introduces a three-layer distributionally robust chance-constrained framework for day-ahead scheduling in e-mobility ecosystems, effectively managing uncertainties without relying on specific probability distributions.

Contribution

It proposes a novel three-layer DRCC model for joint G2V and V2G scheduling, incorporating temporal correlation and providing an exact reformulation for computational efficiency.

Findings

Reduces EVs failing to reach destinations from 272 to 61.

Demonstrates the model's effectiveness in an uncertain environment.

Validates the approach with real data from San Francisco.

Abstract

A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation as a whole in order to overcome range anxiety and create a viable business model for charging stations (CSs). The framework must account for the stochastic nature of all stakeholders' operations, including EV drivers, CSs, and retailers and their mutual interactions. In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation in day-ahead for e-mobility ecosystems. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. To solve the problem, an iterative process is proposed using joint DRCC formulation. To achieve computational traceability, the exact reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of temporal correlation of uncertain PV generation on CSs operation is considered. A simulation study is carried out for an ecosystem of three retailers, nine CSs, and 600 EVs based on real data from San Francisco, the USA. The simulation results show the necessity and applicability of such a scheduling method for the e-mobility ecosystem in an uncertain environment, e.g., by reducing the number of unique EVs that failed to reach their destination from 272 to 61.