PROMETHEE-based Modeling of Endogenous Behavioral Uncertainty of EV Owners

TL;DR

This paper introduces a PROMETHEE-based approach to model the endogenous behavioral uncertainty of EV owners in power distribution system optimization, improving resilience and security.

Contribution

It formulates a distributionally robust chance-constrained model incorporating human factors via PROMETHEE, addressing decision-dependent EV charging demand uncertainty.

Findings

Proposed method outperforms traditional approaches in case studies.

Enhances system resilience and security in EV charging management.

Abstract

The electric vehicle (EV) charging demands (CD) are jointly determined by the EV owners' behavior (i.e., human factor) and the electricity prices (i.e., decisions of distribution system operators (DSO)). However, most existing studies either neglect the decision-dependent nature of EVCD uncertainty or idealistically treat EV owners as perfect decision-makers. This paper formulates the optimal operation of power distribution systems (PDS) as a distributionally robust chance-constrained (DRCC) problem considering EVCDs as endogenous uncertainty (i.e., decision-dependent uncertainty). The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) is introduced to capture the human factor of EV owners in the proposed ambiguity set. Case studies on IEEE test systems demonstrate that the proposed method achieves superior performance compared to deterministic and conventional DRCC approaches, thereby enhancing resilience and security in PDS operations.