Work

A comprehensive framework for predicting electric vehicle's participation in ancillary service markets

Year: 2024

Type: article

Abstract: Electric vehicles (EVs) have significant potential to offer unused capacity in ancillary service markets, providing unique opportunities for market operators to utilise these resources. EVs have a rapid response and high availability, making them a good fit for the frequency containment reserve (FCR) market. However, EV aggregators (EVAGs) must aggregate capacity blocks due to the limited capacity of individual EVs. An application of a supervised machine learning method named XGBoost is suggested to help EVAGs predict the amount of EV participation in the FCR market. The objective is to forecast yearly involvement using data from only a single week, using the game theory method SHapley Additive exPlanations (SHAP) to minimise extra data. The proposed strategy helps aggregators and uses feature engineering to select EVs with high potential to boost revenue. The proposed framework is effective in predicting EV performance in the DK‐2 market, as shown by multiple analyses.

Source: IET Smart Grid

DOI: 10.1049/stg2.12167

Authors: Saeed Naghdizadegan Jahromi, Amir Abdollahi, Ehsan Heydarian‐Forushani, Mehdi Shafiee

Institutions: Shahid Bahonar University of Kerman, Qom University of Technology, Technical and Vocational University

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,340