Energy management in multi-microgrid systems — development and assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The optimal management of energy generation-/consumption in modern distribution systems has gained attention in the smart grid era. This paper presents optimized and coordinated strategies for performing and assessing energy management in multi-microgrid systems. The energy management process is formulated for multi-microgrid systems that simultaneously incorporate several energy generation/consumption units, including different types of distributed generators (DGs), energy storage units, electric vehicles (EVs) and demand response. Due to the probabilistic nature of some loads (e.g. EVs) and generators (e.g. wind turbine and photovoltaic (PV) modules), a novel probabilistic index is defined to measure the success of energy management scenarios in terms of cost minimization. Moreover, by using the new index, common types of energy controllers, such as DGs, storage units, EVs and demand side management are implemented simultaneously and individually, in a system, and the effect of each addition on the defined index and on operational costs is investigated. Finally, the robustness of the process to the load and generation prediction errors is investigated.
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.
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 it