Optimal Operation Scheduling of a Combined Wind-Hydro System for Peak Load Shaving
Bibliographic record
Abstract
Power demand has increased in recent years, posing a significant challenge to power systems. The use of clean energy generated by wind farms (WF) for the operation of pumped hydroelectric energy storage (PHES) is often advocated as a hybrid renewable energy system for peak load reduction, defined as WF-PHES. The objective of this paper is to present a comprehensive optimal operational scheduling strategy-based algorithm for dynamically shaving or reducing peak power loads. For this purpose, a finite horizon scheduling optimization problem has been formulated to optimally control the real-time operation of the WF-PHES that incorporates both predictions of the power load and winds. The main aim of the proposed framework is the scheduling of the whole system operation based on predicted wind speeds and power load profile, while respecting the operational constraints. A three-layered ANN model has been used to forecast the wind speeds and the power load based on historical data. A mathematical decision model considering the power load-shaving and reduction needs of the network for the summer and winter seasons has been developed. The proposed model has been implemented and applied to a case study. The proposed framework has been compared to two distinct non-linear optimization methods: Pyomo with IPOPT solver and Genetic Algorithm (GA) to prove its performance and effectiveness over extensive numerical simulationsNote to Practitioners—This paper is motivated by the problem of reducing the long-term peak load on the electricity grid. A hybrid efficiency system was developed by combining a renewable energy source (wind) with a large-scale storage system. The optimal system operation strategy given by the optimization model shows important advantages in providing a clear view for those making decisions regarding this type of energy system. The developed decision algorithm may be taken as a practical solution to address the development challenges of wind energy and support utilities and power grid managers to increase the penetration of renewable generators as well as reduce peak power loads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".