Design of an Incentive-based Demand Side Management Strategy for Stand-Alone Microgrids Planning
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Bibliographic record
Abstract
Demand Side Management Strategies (DSMSs) can play a significant role in reducing installation and operational costs, Levelized Cost of Energy (LCOE), and enhance renewable energy utilization in Stand-Alone Microgrids (SAMGs). Despite this, there is a paucity in literature exploring how DSMS affects the planning of SAMGs. This paper presents a methodology to design an incentive-based DSMS and evaluate its impact on the planning phase of a SAMG. The DSMS offers two kinds of incentives, a discount in the flat tariff to increase the electrical energy consumption of the users, and an extra payment added to the fare to penalize it. The design of the methodology integrates the optimal energy dispatch of the energy sources, the tariff design, and its sizing. In this regard, the main contribution of this paper is the design of an incentive-based DSMS using a Disciplined Convex approach, and the evaluation of its potential impacts over the planning of SAMG. The methodology also computes how the profits of the investors are modified when the economic incentives vary. A study case shows that the designed DSMS effectively reduces the size of the energy sources, the LCOE, and the payments of the customers for the purchased energy.
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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 | 0.001 |
| 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