A Learning Powered Bi-Level Approach for Dynamic Electricity Pricing

Patrizia Beraldi, Luigi Gallo, Alessandra Rende

2024

Abstract

This paper presents a comprehensive approach to electricity tariff determination by integrating advanced Artificial Intelligence (AI) techniques with Bi-Level (BL) optimization. More specifically, AI techniques are used to obtain accurate forecasts of photovoltaic panel generation, which are then used as input parameters for a deterministic BL problem that models the interaction between a power supplier and a residential prosumer. To handle the high complexity of the BL formulations, the model is first reformulated into a single-level structure, and then linearized using an approach based on the application of the dual reformulation. An intensive experimental phase is carried out on a real case study to test the effectiveness of the proposed methodology and to quantify the impact of the forecast techniques on the supplier strategy.

Paper Citation

in Harvard Style

Beraldi P., Gallo L. and Rende A. (2024). A Learning Powered Bi-Level Approach for Dynamic Electricity Pricing. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-681-1, SciTePress, pages 390-397. DOI: 10.5220/0012465700003639

in Bibtex Style

@conference{icores24,
author={Patrizia Beraldi and Luigi Gallo and Alessandra Rende},
title={A Learning Powered Bi-Level Approach for Dynamic Electricity Pricing},
booktitle={Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2024},
pages={390-397},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012465700003639},
isbn={978-989-758-681-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - A Learning Powered Bi-Level Approach for Dynamic Electricity Pricing
SN - 978-989-758-681-1
AU - Beraldi P.
AU - Gallo L.
AU - Rende A.
PY - 2024
SP - 390
EP - 397
DO - 10.5220/0012465700003639
PB - SciTePress