Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems
Bibliographic record
Abstract
A linear programming (LP) routine was implemented to model optimal energy storage dispatch schedules for peak net load management and demand charge minimization in a grid-connected, combined photovoltaic-battery storage system (PV+ system).The LP leverages PV power output and load forecasts to minimize peak loads subject to elementary dynamical and electrical constraints of the PV+ system.Battery charge/discharge were simulated over a range of two PV+ system parameters (battery storage capacity and peak load reduction target) to obtain energy cost for a time-of-use pricing schedule and the net present value (NPV) of the battery storage system.The financial benefits of our optimized energy dispatch schedule were compared with basic offpeak charging/on-peak discharging and real-time load response dispatch strategies that did not use any forecast information.The NPV of the battery array increased significantly when the battery was operated on the optimized schedule compared to the off-peak/on-peak and real time dispatch schedules.These trends were attributed to increased battery lifetime and reduced demand charges attained under the optimized dispatch strategy.Our results show that Lithiumion batteries can be a financially viable energy storage solution in demand side, energy cost management applications at an installed cost of about $400 -$500 per kWh (approximately 40-50% of 2011 market prices).The financial value of forecasting in energy storage dispatch optimization was calculated as a function of battery capacity ratio.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".