Optimizing Battery Price Arbitrage in Alberta’s Electricity Market
Bibliographic record
Abstract
This thesis employs a mixed‐integer linear programming (MILP) framework to explore how battery‐based systems, equipped with vehicle‐to‐grid (V2G) capabilities, can manage charging and discharging schedules in order to minimize, or even reverse, their net electricity costs under varying price conditions. The analysis begins with a single electric vehicle (EV) that is required to maintain 80% of its battery state of charge (SOC) for daily use. Through a cost‐minimization approach, the EV draws energy during lower‐price windows and returns stored energy back to the grid, under V2G, during peak pricing, potentially deriving its net cost below zero. The system constrained is then loosened by shifting the analysis to a fleet of electric buses (EBF), each with a larger battery yet bound by strict route timetables. Although these buses have greater storage capacity, enabling them to take advantage of price spreads, their scheduled service hours often coincide with higher pricing windows, limiting opportunities to fully optimize charging or discharging. As a result, notable cost reductions are achieved, but the magnitude of these savings is largely determined by how effectively off‐peak and on‐peak price intervals align with their operating times. Finally, the thesis analyzes a data center that is free from mobility requirements and maintains a large‐scale battery system perpetually connected to the grid. Unlike the EV and buses, the data center has no fixed operating windows to satisfy; it continuously monitors real‐time electricity prices and adjusts its charging and discharging accordingly. By exploiting the largest price spreads, the data center achieves the lowest overall net cost among the three systems, underscoring how full‐time grid access and higher storage capacity amplify the benefits of arbitrage. All these results show that when a MILP model is used, an adaptive and price‐responsive charging strategy can transform battery systems from mere loads into revenue‐generating assets, whether in personal vehicles, fleet applications, or large‐scale computational facilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".