796. A Modified Simplex Algorithm to Efficiently Explore Near-Optimal Spaces
Invited abstract in session MC-44: Multi-criteria energy systems modelling, stream Energy Economics & Management.
Monday, 12:30-14:00Room: Newlyn 1.01
Authors (first author is the speaker)
| 1. | Christoph Funke
|
| Reliability and Risk Engineering, ETH Zürich | |
| 2. | Linda Brodnicke
|
| Mechanical and Process Engineering, ETH Zurich | |
| 3. | Francesco Lombardi
|
| TU Delft | |
| 4. | Giovanni Sansavini
|
| ETH |
Abstract
Modeling to generate alternatives (MGA) is an increasingly popular method in energy system optimization. MGA explores the near-optimal space, namely, system alternatives whose costs are within a certain fraction of the globally optimal cost. Widespread MGA adoption is hampered by its additional computational burden. Current MGA methods identify boundary points of the near-optimal space through repeated, independent optimization problems. Hundreds of model runs are usually required, and such individual runs are often inefficient because they repeat calculations or retrace previous trajectories. In this study, we transcend such limitations by introducing a novel algorithm called Funplex, which uses methods from multi-objective Simplex to optimize many MGA objectives with minimal computational redundancy. For a simple linear-programming energy hub case study, we show that the proposed algorithm is five times faster than existing methods and yields higher-quality near-optimal spaces. Furthermore, sensitivity analyses suggest that the algorithm scales well with the number of investment variables, making it promising for capacity planning models. Future developments based on advanced versions of Simplex may overcome computational barriers and make MGA accessible and standard among energy modeling teams.
Keywords
- Optimization Modeling
- OR in Energy
- Convex Optimization
Status: accepted
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