Towards Net-Zero Emission in Hybrid Power Microgrids: A Data-Driven MPC-Based Optimization Approach
Bibliographic record
Abstract
This paper presents a data-driven model predictive control (MPC) framework aimed at reducing carbon emissions and optimizing operational costs in hybrid power microgrids. The proposed approach leverages a neural state-space model (NSSM) to capture the complex, nonlinear dynamics of voltage levels across buses, ensuring system stability and robustness under varying renewable generation. By integrating real-time forecasts of wind, solar power, load demands, and time-of-use electricity (TOU) prices, the MPC optimally dispatches generation set points across combined cycle gas turbines (CCGT), nuclear generators, and battery energy storage systems (BESS), effectively reducing reliance on high-emission grid imports. Results demonstrate the MPC’s capability to significantly reduce carbon emissions while maintaining reliable, cost-efficient operation. This work supports ongoing industry efforts to achieve net-zero emission targets, offering a viable pathway for sustainable energy management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".