EURO 2024 Copenhagen
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2536. Optimal operation of a residential smart grid with short-term and long-term storage

Invited abstract in session TA-22: Optimization of energy storage systems, stream Energy Management.

Tuesday, 8:30-10:00
Room: 81 (building: 116)

Authors (first author is the speaker)

1. Eléa Prat
DTU
2. Pierre Pinson
Dyson School of Design Engineering, Imperial College London
3. Richard Lusby
DTU Management, Technical University of Denmark
4. Riwal Plougonven
LMD-IPSL, Ecole Polytechnique, Institut Polytechnique de Paris, ENS, PSL Research University, Sorbonne Université, CNRS
5. Jordi Badosa
LMD-IPSL, Ecole Polytechnique, Institut Polytechnique de Paris, ENS, PSL Research University, Sorbonne Université, CNRS
6. Philippe Drobinski
LMD-IPSL, Ecole Polytechnique, Institut Polytechnique de Paris, ENS, PSL Research University, Sorbonne Université, CNRS

Abstract

To enhance buildings' energy independence and carbon neutrality, technologies like energy storage and local renewables are increasingly deployed. Among these, long-term geothermal storage for residential buildings is emerging and poses new challenges in multi-timescale optimization.
We investigate the operation of a novel residential smart grid, based on a real project. It integrates electricity and heat systems via a heat pump and includes short-term electricity storage, long-term thermal storage, photovoltaics, and solar thermal assets. While the operation is optimized daily, seasonal considerations are crucial, especially to ensure that the thermal storage fills during summer. The question then is how to account for long-term effects in the daily optimization.
Our study relates to two key domains of operations research: inventory management and resource allocation. Utilizing a year-long real dataset, we explore different methods to guide the energy storage levels by day's end. These methods range from historical data-based thresholds to rolling-horizon strategies, assessing the impact of optimization window length. We also evaluate the relaxation of end-of-horizon constraints using penalties.
We analyze the strengths and weaknesses of each method, acknowledging the challenge of unreliable long-term forecasts. These insights facilitate decision-making for residential energy systems and provide valuable implications for capacitated warehouse problems in other contexts.

Keywords

Status: accepted

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