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2345. Modified Simplex Algorithm to Efficiently Explore Near-Optimal Spaces
Invited abstract in session WC-38: Advances in algorithms and applications for linear and convex optimization, stream Conic Optimization: Theory, Algorithms, and Applications.
Wednesday, 12:30-14:00Room: 34 (building: 306)
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 (NOS), namely, system alternatives whose costs are within a certain fraction of the globally optimal cost. These alternatives may be preferred by real-world stakeholders due to intangible factors. Widespread MGA adoption is hampered by its additional computational burden. Current MGA methods identify boundary points of the NOS 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 presentation, we introduce a novel algorithm, called Funplex, which uses elements from multi-objective Simplex to optimize many MGA objectives with minimal computational redundancy . On a simple linear program energy hub case study, Funplex is ten times faster than existing methods and yields higher-quality NOSs. Sensitivity analyses suggest that Funplex scales well with the number of investment variables, making it promising for capacity planning models. The current implementation uses a full multi-objective tableau and therefore requires high memory and has poor stability on large models. Nonetheless, Funplex is a proof-of-concept that demonstrates the untapped potential of new solver methods. Further research on the topic may make MGA more standard and accessible among modeling teams.
Keywords
- Algorithms
- OR in Energy
- Programming, Multi-Objective
Status: accepted
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