1737. Estimating maintenance cost of offshore electrical substations
Invited abstract in session TA-27: Applications of Optimization under Uncertainty, stream Stochastic and Robust optimization.
Tuesday, 8:30-10:00Room: Maurice Keyworth G.02
Authors (first author is the speaker)
| 1. | Solène Delannoy-Pavy
|
| CERMICS Ecole des Ponts ParisTech | |
| 2. | vincent leclere
|
| CERMICS, Ecole des Ponts | |
| 3. | Axel Parmentier
|
| CERMICS, Ecole des Ponts ParisTech | |
| 4. | Manuel Ruiz
|
| RTE |
Abstract
Offshore wind power is rapidly expanding as part of the green transition. The French Transmission System Operator (TSO) owns and maintains offshore electrical substations that connect wind farms to the grid. We present a decision support tool to estimate maintenance costs for various strategic decisions, such as substation design selection and stock management policies.
We formulate a discrete, finite-horizon Markov Decision Process where states represent substation degradation levels, and actions correspond to maintenance operations. Costs are derived from penalties incurred by the TSO, proportional to untransmitted production beyond a specified quota of free maintenance days. Our focus is on heavy maintenance, which requires substation shutdowns. Maintenance decisions must consider stochastic access conditions, influenced by factors like wave height and wind speed.
To address this uncertainty, we introduce two decision rules that account for random access conditions, simplifying the problem to an open-loop format. We propose a fast approximate solution method using Sample Average Approximation. Additionally, we extend the model to a closed-loop formulation to represent different maintenance types. To manage model ambiguity, we incorporate Bayesian and Distributionally Robust Optimization.
Keywords
- Stochastic Optimization
- Robust Optimization
- OR in Energy
Status: accepted
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