Demand side management for the future smart grid
Bibliographic record
Abstract
To achieve a high level of reliability and robustness in power systems, the grid is usually designed for the peak demand rather than the average demand. This usually results in an under-utilized system. Demand side management (DSM) programs can be adopted to shape the load pattern of the users to better utilize the available power generation capacity and to prevent installing new generation and transmission infrastructures. In this thesis, we propose different algorithms for DSM. First, we focus on the problem of maximizing the social welfare of the users. We consider a scenario where the users are equipped with automated control units and are able to make price-responsive decisions. We propose a Vickrey-Clarke-Groves (VCG) mechanism to maximize the social welfare of the users. Subsequently, we focus on developing a novel automated load scheduling algorithm to minimize the energy expenses of the user. The proposed algorithm takes into account the effects of the load uncertainties in future time slots. Moreover, the operational constraints of different types of appliances including must-run appliances, and interruptible and non-interruptible controllable appliances are studied. Next, we study how the utility company can set price values for different times of a day such that the peak-to-average ratio (PAR) of the load is minimized. We also consider the effects of the uncertainty regarding the price-responsiveness of the users. To simulate the likely behavior of the users in response to different price values for different times of the day, we propose the use of a system simulator unit. We propose two pricing algorithms based on stochastic approximation aiming to minimize the PAR of the aggregate load. Finally, we consider systems with high penetration of renewable energy resources. To tackle the reverse power flow problem associated with these systems, we propose a joint load scheduling and trading algorithm. This algorithm encourages the users to sell their excess generation to their neighboring users which mitigates the reverse power flow problem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".