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547. Multistage stochastic optimization of an elementary hydrogen infrastructure
Invited abstract in session WB-9: Production Optimization and Supply Chain Management of Green Hydrogen under Uncertainties, stream Energy Markets.
Wednesday, 10:30-12:00Room: 10 (building: 116)
Authors (first author is the speaker)
| 1. | Raian Noufel LEFGOUM
|
| Cermics and Persee | |
| 2. | Jean-Philippe Chancelier
|
| CERMICS Ecole des Ponts et Chaussées, Université Paris Est | |
| 3. | Michel De Lara
|
| École des Ponts ParisTech | |
| 4. | Pierre Carpentier
|
| UMA, ENSTA | |
| 5. | Sezin Afsar
|
| University of Oviedo |
Abstract
The desired transition towards a hydrogen economy requires hydrogen costs to come down, through optimal choices of infrastructure design and operation.
In this talk, we present an approach based on multistage stochastic optimization mixing design choices with operational decisions taken on an hourly basis.
We consider an elementary hydrogen infrastructure which consists of an electrolyzer, a compressor and a storage to serve a transportation demand.
This infrastructure is powered by three sources of energy (on site photovoltaics, renewable electricity through power purchase agreement, power grid).
The modelling of the electrolyser covers its different functioning modes and the nonlinear relation between the production of hydrogen and the electricity consumption.
The optimization problem is to minimize the operational costs over a week (while emphasizing the use of renewable sources), by making hourly decisions in an uncertain context.
We consider uncertainties affecting on site photovoltaics production and hydrogen demand. We formulate a multistage stochastic optimization problem, and we develop suitable algorithms based on dynamic programming. We present numerical results for a given infrastructure design. Then, we consider various combinations of infrastructure design and their subsequent optimal operation.
With this, we discuss the optimal sizing of equipment, especially the sensitivity of electrolyzer and storage designs to the uncertainties.
Keywords
- Supply Chain Management
- Stochastic Optimization
Status: accepted
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