Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study
By shifting towards renewable energy sources, manufacturing facilities can significantly reduce their carbon footprint. This environmental issue can be addressed by developing sustainable production through on-site renewable electricity generation and demand-side management policies. In this study,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/14/5433 |
| _version_ | 1797589479124369408 |
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| author | Mohamed Habib Jabeur Sonia Mahjoub Cyril Toublanc |
| author_facet | Mohamed Habib Jabeur Sonia Mahjoub Cyril Toublanc |
| author_sort | Mohamed Habib Jabeur |
| collection | DOAJ |
| description | By shifting towards renewable energy sources, manufacturing facilities can significantly reduce their carbon footprint. This environmental issue can be addressed by developing sustainable production through on-site renewable electricity generation and demand-side management policies. In this study, the energy required to power the manufacturing system is obtained from different energy sources: the conventional grid, on-site renewable energy, and an energy storage system. The main objective is to generate a production schedule for a flexible multi-process and multi-product manufacturing system that optimizes the utilization and procurement of electricity without affecting the final demand. A mathematical programming model is proposed to minimize both the total production costs and energy costs, considering a time-of-use pricing policy and an incentive-based program. The uncertainty in renewable energy generation, specifically under the worst-case scenario, is taken into account and the model is transformed into a robust two-stage optimization model. To solve this model, a decomposition approach based on a genetic algorithm is applied. The effectiveness of the proposed model and algorithm is tested on a real industry case involving feed-animal products. A sensitivity analysis is conducted by modifying problem parameters. Finally, a comparison with the nested Column and Constraint Generation algorithm is performed. The obtained results from these analyses validated the proposed model and algorithm. |
| first_indexed | 2024-03-11T01:07:16Z |
| format | Article |
| id | doaj.art-fb6e38c8b8974d0b8e1ce3ae0e4a45f8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T01:07:16Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fb6e38c8b8974d0b8e1ce3ae0e4a45f82023-11-18T19:10:21ZengMDPI AGEnergies1996-10732023-07-011614543310.3390/en16145433Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case StudyMohamed Habib Jabeur0Sonia Mahjoub1Cyril Toublanc2Oniris, INRAE, STATSC, 44300 Nantes, FranceOniris, Nantes University, LEMNA, CS 82225, 44322 Nantes, FranceOniris, Nantes University, CNRS, GEPEA, UMR 6144, F-44000 Nantes, FranceBy shifting towards renewable energy sources, manufacturing facilities can significantly reduce their carbon footprint. This environmental issue can be addressed by developing sustainable production through on-site renewable electricity generation and demand-side management policies. In this study, the energy required to power the manufacturing system is obtained from different energy sources: the conventional grid, on-site renewable energy, and an energy storage system. The main objective is to generate a production schedule for a flexible multi-process and multi-product manufacturing system that optimizes the utilization and procurement of electricity without affecting the final demand. A mathematical programming model is proposed to minimize both the total production costs and energy costs, considering a time-of-use pricing policy and an incentive-based program. The uncertainty in renewable energy generation, specifically under the worst-case scenario, is taken into account and the model is transformed into a robust two-stage optimization model. To solve this model, a decomposition approach based on a genetic algorithm is applied. The effectiveness of the proposed model and algorithm is tested on a real industry case involving feed-animal products. A sensitivity analysis is conducted by modifying problem parameters. Finally, a comparison with the nested Column and Constraint Generation algorithm is performed. The obtained results from these analyses validated the proposed model and algorithm.https://www.mdpi.com/1996-1073/16/14/5433production schedulingdemand-side managementonsite renewableuncertaintyrobust optimizationgenetic algorithm |
| spellingShingle | Mohamed Habib Jabeur Sonia Mahjoub Cyril Toublanc Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study Energies production scheduling demand-side management onsite renewable uncertainty robust optimization genetic algorithm |
| title | Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study |
| title_full | Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study |
| title_fullStr | Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study |
| title_full_unstemmed | Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study |
| title_short | Sustainable Production Scheduling with On-Site Intermittent Renewable Energy and Demand-Side Management: A Feed-Animal Case Study |
| title_sort | sustainable production scheduling with on site intermittent renewable energy and demand side management a feed animal case study |
| topic | production scheduling demand-side management onsite renewable uncertainty robust optimization genetic algorithm |
| url | https://www.mdpi.com/1996-1073/16/14/5433 |
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