Optimal Energy and Reserve Market Management in Renewable Microgrid-PEVs Parking Lot Systems: V2G, Demand Response and Sustainability Costs
Bibliographic record
Abstract
Vehicle-to-grid (V2G) technology heralds great promise as a demand-side resource to contribute to more efficient grid management and promote the use of decentralized renewable energy. In this light, we propose a new optimization model for the sustainable energy and reserve market management in renewable-driven microgrid (RMG) plug-in electric vehicles (PEVs) parking lot systems. The RMG is composed of a hybrid photovoltaic/wind/hydrogen energy and storage system, along with local dispatchable generation units and bidirectional grid connection. The RMG is coupled to a smart PEVs parking lot, which is equipped with grid-to-vehicle (G2V) and V2G technologies allowing for not only PEVs aggregation and control but also optimal allocation of energy resources. Time-of-use (TOU) prices are considered in a demand response program (DRP) to enhance both economic and environmental performances by encouraging end-users to shift their energy demands from peak to off-peak time periods. Additionally, the model accounts for an economic incentive to PEVs owners to compensate for battery degradation. The integrated system eco-efficiency is evaluated through the application of the novel life cycle assessment-based Eco-cost indicator. The resulting mixed-integer linear programming model to minimize sustainability costs is implemented in GAMS and solved to global optimality. Different case studies are performed to demonstrate the effectiveness of the proposed modelling approach. Energy analyses results reveal that the optimal G2V-V2G operation, allied to TOU prices in a DRP, and reserve market management can reduce around 42% the energy and environmental costs of the RMG-PEVs parking lot system.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".