A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts. (1st September 2017) - Craparo, Emily; Karatas, Mumtaz; Singham, Dashi I.
Highlights: Scenario-robust optimization provides superior solutions for microgrid operations. Ensemble weather forecasts provide valuable input data for planning models. Simulation techniques can generate large amounts of realistic data for testing. Longer planning horizons do not necessarily lead to better plans. Abstract: Hybrid microgrids that use renewable energy sources can improve energy security and islanding time while reducing costs. One potential beneficiary of these systems is the U.S. military, which can seek to improve energy security when operating in isolated areas by using a microgrid rather than relying on a fragile (or nonexistent) commercial network. Renewable energy sources can be intermittent and unpredictable, making it difficult to plan operations of a microgrid. We describe a scenario-robust mixed-integer linear program designed to utilize ensemble weather forecasts to improve the performance of a hybrid microgrid containing both renewable and traditional power sources. We exercise our model to quantify the benefit of using ensemble weather forecasts, and we predict the optimal performance of a hypothetical grid containing wind turbines by using simulated realistic weather forecast scenarios based on data. Because forecast quality degrades with lead time, we perform a sensitivity analysis to determine which planning horizon results in the best performance. Our results show that, for day-ahead planning, longer planning horizons outperform shorter planning horizons in terms of cost of operations, but this improvement diminishes as the planning horizon lengthens.