Abstract
Energy storage is crucial to increase renewable energy adoption in construction. By optimizing their control strategies, operational costs decrease and return on investment improves. Model Predictive Controls (MPC) have been used to optimize the use of energy storage but are costly to implement. This paper presents an MPC with a generalized mathematical model for electrical and thermal storage. A methodology is introduced to account for physical restrictions. Three evolutionary algorithms were compared for the optimization and a Genetic Algorithm was found to best reduce the energy bill with average daily savings of 38.7 %.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 European Conference on Computing in Construction |
| Editors | Marijana Srećković, Mohamad Kassem, Ranjith Soman, Athanasios Chassiakos |
| Publisher | European Council on Computing in Construction (EC3) |
| Pages | 703-710 |
| Number of pages | 8 |
| ISBN (Print) | 9789083451305 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |
| Event | European Conference on Computing in Construction, EC3 2024 - Chania, Greece Duration: 14 Jul 2024 → 17 Jul 2024 |
Publication series
| Name | Proceedings of the European Conference on Computing in Construction |
|---|---|
| Volume | 2024 |
| ISSN (Electronic) | 2684-1150 |
Conference
| Conference | European Conference on Computing in Construction, EC3 2024 |
|---|---|
| Country/Territory | Greece |
| City | Chania |
| Period | 14/07/24 → 17/07/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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