A new approach on quantification of flexibility index in multi-carrier energy systems towards optimally energy hub management
| Autor: | Maryam Azimi, Abolfazl Salami |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Energy carrier
Flexibility (engineering) Mathematical optimization Computer science 020209 energy Mechanical Engineering Scheduling (production processes) Flexibility Index 02 engineering and technology Building and Construction Pollution Industrial and Manufacturing Engineering Demand response General Energy 020401 chemical engineering Margin (machine learning) Search algorithm 0202 electrical engineering electronic engineering information engineering 0204 chemical engineering Electrical and Electronic Engineering Energy (signal processing) Civil and Structural Engineering |
| Zdroj: | Energy. 232:120973 |
| ISSN: | 0360-5442 |
| Popis: | Multi-carrier energy systems (MCESs) are identified as intermediate frameworks among different energy carriers aiming to optimally meet diverse energy demands. In this study, a novel robust-based flexibility evaluation method is proposed for MCESs to quantify the maximum potentiality of the energy hub to compensate for the maximum uncertainty. For this purpose, a singular flexibility index is first calculated via the definition of apposite corrective actions, including purchasing the supplementary input carriers and implementing the demand response programs (DRPs). Then, given various individual uncertainty sets, a new bi-level optimization framework is introduced to achieve the optimal and deterministic scheduling of MCESs in the upper-level and determining the largest uncertainty radiuses in the lower-level. The demonstrated method is formulated as a second-order cone programming and is solved through an iteration-based search algorithm. The presented structure is implemented for a sample energy hub to confirm its extensive capability to apply various flexible options as compared to the information gap decision theory (IGDT). The maximum uncertainty margin in the electrical load is averagely equal to 79% of the predicted demand. The proposed method could be also fruitful for the robust scheduling of the energy hub in a worst-case scenario while considering the maximum uncertainty. |
| Databáze: | OpenAIRE |
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