Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response
Bibliographic record
Abstract
Rising energy demands, economic challenges, and the urgent need to address climate change have led to the emergence of a market wherein consumers can both purchase and sell electricity to the grid. This market leverages diverse energy sources and energy storage systems to achieve significant cost savings for consumers while providing critical grid support for utilities. In this study, an energy management system has been employed to tackle the optimization problem associated with various energy sources. This approach relies on mixed-integer linear programming (MILP) to optimize energy utilization while adhering to diverse constraints, yielding a feasible energy solution. This model is applied to real-world energy system consumption data and forecasts the most cost-effective day-ahead energy plans for different types of loads engaged in demand response. Furthermore, time-based charging and discharging strategies for electric vehicles and energy storage systems are considered, conducting a comprehensive analysis of energy costs across various storage devices. Our findings demonstrate that implementing this model can lead to an 18.26% reduction in operational costs when using lithium batteries and a remarkable 14.88% reduction with lead–acid batteries, particularly when integrating solar power and an EV into the system, while GHG is reduced by 36,018 grams/day for a load of 25 kW in one particular scenario. However, the analysis reveals that integrating wind power is not economically viable due to its comparatively higher operational costs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".