Flexibility Service for Balance Responsible Parties’ Industrial Customers: A Day-Ahead Cost Optimization Approach Using Price Forecasting
Electricity-intensive industries are highly impacted by electricity prices and their fluctuations. While these industries can change their consumption profiles to avoid peak price periods, they must also adhere to production schedules dictated by their unique manufacturing processes and related cons...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10990257/ |
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| Summary: | Electricity-intensive industries are highly impacted by electricity prices and their fluctuations. While these industries can change their consumption profiles to avoid peak price periods, they must also adhere to production schedules dictated by their unique manufacturing processes and related constraints. Balance Responsible Parties (BRPs), seek to optimize the electricity costs of their portfolio of customers and have limited access to real-time industrial flexibility. So, the concept of Daily Industrial Flexibility (DIF) is introduced as a trade-off between real-time flexibility and predetermined industrial schedules. Each factory can propose different fixed day-ahead consumption profiles to the BRP, each with a specific activation cost. In this context, this paper proposes a day-ahead flexibility service to minimize the BRP’s costs of electricity purchase on the day-ahead market, activating DIFs within the portfolio of industrial customers. A forecasting model for the electricity price is implemented and an optimization model is formulated to minimize day-ahead portfolio costs. Finally, the case study of a Catalan BRP is presented, considering DIF offers from its largest customers. Results demonstrate that the proposed approach enables accurate predictions of the BRP’s customers operation before DAM closure, successfully identifying alternative schedules to reduce portfolio costs. |
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| ISSN: | 2169-3536 |