k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization
| Autor: | Emanuele Martelli, Marco Gabba, Michele Rossi, Marco Freschini, Matteo Zatti, Agostino Gambarotta, Mirko Morini |
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| Rok vydání: | 2019 |
| Předmět: |
Multi-energy systems
Mathematical optimization Linear programming Computer science 020209 energy Design optimization 02 engineering and technology Industrial and Manufacturing Engineering District energy systems Set (abstract data type) 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering 0204 chemical engineering Electrical and Electronic Engineering Extreme days Typical days Representation (mathematics) Cluster analysis Selection (genetic algorithm) Civil and Structural Engineering Mechanical Engineering Building and Construction Pollution Sizing General Energy Systems design Integer (computer science) |
| Zdroj: | Energy. 181:1051-1063 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2019.05.044 |
| Popis: | When optimizing the design of multi-energy systems, the operation strategy and the part-load behavior of the units must be considered in the optimization model, which therefore must be formulated as a two-stage problem. In order to guarantee computational tractability, the operation problem is solved for a limited set of typical and extreme periods. The selection of these periods is an important aspect of the design methodology, as the selection and sizing of the units is carried out on the basis of their optimal operation in the selected periods. This work proposes a novel Mixed Integer Linear Program clustering model, named k-MILP, devised to find at the same time the most representative days of the year and the extreme days. k-MILP allows controlling the features of the selected typical and extreme days and setting a maximum deviation tolerance on the integral of the load duration curves. The novel approach is tested on the design of two different multi-energy systems (a multiple-site university Campus and a single building) and compared with the two well-known clustering techniques k-means and k-medoids. Results show that k-MILP leads to a better representation of both typical and extreme operating conditions guiding towards more efficient and reliable designs. |
| Databáze: | OpenAIRE |
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