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1403. Assessing the performance of optimized energy communities under uncertainties
Invited abstract in session MC-53: Sustainable Energy, stream Sustainable and Resilient Systems.
Monday, 12:30-14:00Room: 8007 (building: 202)
Authors (first author is the speaker)
| 1. | Nathalie Frieß
|
| Department of Operations and Information Systems, University of Graz | |
| 2. | Ulrich Pferschy
|
| Department of Operations and Information Systems, University of Graz | |
| 3. | Joachim Schauer
|
| Department of Software Design und Security, FH Joanneum |
Abstract
Renewable Energy Communities (RECs) can provide a useful contribution to the decarbonization of the energy sector if they bring about an actual change in physical electricity flows, i.e., if they lead to an optimized use of locally produced energy. Main tools for reaching this objective are load shifting and intelligent battery management. Based on forecasts of production and consumption profiles, the associated operational decisions can be optimized with the goal of improving the overall performance of the community. The strong interdependence between the community members’ individual resources prohibits the use of simple heuristic decision rules for this purpose. As actual conditions may well deviate from the forecasts used for optimization, not all optimized decisions will yield the intended outcome. Therefore, we developed a Model Predictive Control inspired planning framework that comprises an optimization model and a simulation model. Using the most recent available short-term forecasts as input, the optimization model is solved for a planning period of 96 time steps. The computed optimization output for the upcoming time step then serves as input for the simulation model, which models the actual real-world outcome of the planning decisions for the current time step. The introduced framework can be used to generate realistic performance measures and to reach a better understanding of the benefits of optimization-based decision-making in RECs.
Keywords
- OR in Energy
- Energy Policy and Planning
- Forecasting
Status: accepted
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