Microgrid energy management: how uncertainty modelling impacts economic performance
Bibliographic record
Abstract
Short‐term electricity prices are key economic input to model the optimal operation of grid‐connected microgrids. In competitive electricity markets, these prices are not known in advance, and need to be forecasted. Price forecasts, however, have uncertainty, and thus, their errors will impact economic gains. Three main approaches have been employed in the literature to mitigate the uncertainties associated with price forecasts, i.e. rolling horizon optimisation techniques, interval optimisation and scenario‐based methods. In this study, we investigate the economic values of using these approaches, as well as the combination of them, in the operation of microgrids. This is to inform microgrid operators on how to determine which approach should be adopted under different circumstances. Therefore, we first implement point, interval and scenario forecasting models for electricity market prices. The generated forecasts are then fed into a deterministic, robust and stochastic optimisation models for operation scheduling of a typical microgrid. The changes in total energy costs of the microgrid are then evaluated. The simulation results show that while the performance of different methods depends on the volatility of market prices, the model with point price forecasts and a rolling horizon operation scheduling either outperforms other methods or does equally well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".