Distributionally Robust Day-ahead Scheduling for Power-traffic Network under a Potential Game Framework

TL;DR

This paper develops a distributionally robust optimization framework for day-ahead scheduling of power and traffic networks with electric vehicles, accounting for uncertainties in renewable generation and traffic demand, using a potential game approach.

Contribution

It introduces a novel distributionally robust model incorporating a potential game framework for coupled power-traffic networks under uncertainty.

Findings

Achieves less conservative and more optimal scheduling strategies.

Effectively models the coupling between power and traffic networks.

Demonstrates robustness against uncertainties in renewable energy and traffic demand.

Abstract

Widespread utilization of electric vehicles (EVs) incurs more uncertainties and impacts on the scheduling of the power-transportation coupled network. This paper investigates optimal power scheduling for a power-transportation coupled network in the day-ahead energy market considering multiple uncertainties related to photovoltaic (PV) generation and the traffic demand of vehicles. The crux of this problem is to model the coupling relation between the two networks in the day-ahead scheduling stage and consider the intra-day spatial uncertainties of the source and load. Meanwhile, the flexible load with a certain adjustment margin is introduced to ensure the balance of supply and demand of power nodes and consume the renewable energy better. Furthermore, we show the interactions between the power system and EV users from a potential game-theoretic perspective, where the uncertainties are characterized by an ambiguity set. In order to ensure the individual optimality of the two networks in a unified framework in day-ahead power scheduling, a two-stage distributionally robust centralized optimization model is established to carry out the equilibrium of power-transportation coupled network. On this basis, a combination of the duality theory and the Benders decomposition is developed to solve the distributionally robust optimization (DRO) model. Simulations demonstrate that the proposed approach can obtain individual optimal and less conservative strategies.