556. Optimal Microgrid Sizing and Operation with Distributionally Robust Optimization
Invited abstract in session WD-44: Advanced methods for designing and operating energy systems under uncertainty, stream Energy Economics & Management.
Wednesday, 14:30-16:00Room: Newlyn 1.01
Authors (first author is the speaker)
| 1. | Gulfem Er
|
| Mathematical Science, University of Southampton | |
| 2. | Christine Currie
|
| School of Mathematics, University of Southampton | |
| 3. | Gevorg Stepanyan
|
| Mathematical Science, University of Southampton |
Abstract
In this study, we examine the optimal sizing and operation of a grid-connected microgrid system that includes wind turbines, photovoltaic (PV) panels, and batteries. Microgrids are small-scale power systems that enhance energy resilience, integrate renewable energy sources, and reduce dependency on centralized grids. The main challenge arises from the uncertainties from renewable energy sources, such as the intermittency of wind and solar production, which leads to significant variability in power generation. To address these uncertainties we employ a two-stage distributionally robust optimization approach. In the first stage, we make sizing decisions to minimize component-related costs. In the second stage, we focus on operational decisions regarding battery management and power exchanges with the grid to minimize operational costs. This approach reduces the risk of incorrectly sizing system components by considering the worst-case distribution within the ambiguity set. Finally, we reformulate the problem into a tractable Mixed-Integer Linear Programming (MILP) formulation, which we solve using Gurobi Python. We use the presented model to design a microgrid based on similar systems in the UK, utilizing publicly available data from Octopus Energy Ltd.
Keywords
- OR in Energy
- Robust Optimization
Status: accepted
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