Economic Control for a Residential Photovoltaic-Battery System by Combining Stochastic Model Predictive Control and Improved Correction Strategy
Bibliographic record
Abstract
Abstract Photovoltaic (PV) power generation can help reduce households’ electricity from the power grid and thus reduce electricity bills. However, due to the intermittence and time-varying nature of PV power generation, part of the clean energy will be wasted. Especially in some places where PV power is allowed to be sold to the power grid, the PV power that exceeds the feed-in limit will be curtailed to reduce the pressure on the infrastructure of the power grid. Battery energy storage systems (BESSs) as energy buffers have attracted increasing attention to help improve the penetration of PV power to households. This paper presents an adaptive energy management method to minimize the energy cost of residential PV-battery systems. First, the uncertainty of the predictive electricity demand and PV power supply is modeled. Then a stochastic model predictive control (SMPC) strategy is used to determine the optimal power flow of the system. Due to the deviation between the predictive input values and the actual ones, the power flow from SMPC is adjusted based on the improved correction strategy (ICS) proposed in this paper. By comparing with the other two methods (one considers the uncertainty and the other does not), the proposed method can increase the economic benefits of the system by 18% and 63%, respectively. The wasted PV power that exceeds the feed-in limit can also be reduced by 24% and 31%. This verifies the effectiveness of the proposed method to improve the system's economic benefits and self-consumption of clean energy.
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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) | 0.001 | 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 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".