Application of a goal programming algorithm to incorporate environmental requirements in a multi-objective Columbia River Treaty Reservoir optimization model
Bibliographic record
Abstract
Due to large variability in streamflows and a lack of adequate reservoir storage capability in US territories, a treaty for the Columbia River was established between Canada and the USA in 1964.However, the treaty only considers power generation and flood control, and to address environmental issues, supplemental operating agreements have been signed each year since the 1990s.In this paper, we present a goal programming (GP) optimization algorithm to model the terms and conditions of the Supplemental Operating Agreements of the Columbia River Treaty (CRT) between BC Hydro and "United States Entities".The GP technique is a multi-objective programming method that has been used in many different fields including reservoir optimization.After its introduction in 1961, it has been used extensively and is considered a robust modeling technique.The GP algorithm we have developed models the multi-objective problem using a combination of lexicographic and weighted goal-programming techniques.Case studies using four scenarios were performed to assess the satisfaction of environmental requirements for different target flow requirements at the US-Canadian border of the Columbia River.The GP algorithm we have developed allows for the constraints to deviate from a target value.This in turn provides modeling flexibility to handle infeasibility, typically encountered when hard constraints are included in the formulation of the optimization problem.GP can be used to investigate the trade-offs between multiple objectives by minimizing the deviation from user-specified target levels.In addition, the goal-programming formulation can represent more realistic real-time operational situations of a complex multi-reservoir system like the BC Hydro system.En raison de la grande variabilit de l'coulement fluvial et de la faible capacit de stockage des rservoirs en territoire amricain, le Canada et les tats-Unis d'Amrique ont conclu le Trait du fleuve Columbia en 1964.Or, comme celui-ci ne porte que sur de la production d'nergie hydrolectrique et la prvention des crues, des conventions d'exploitation supplmentaires traitant de proccupations d'ordre environnemental ont t signes chaque anne partir des annes 1990.Nous prsentons ici un algorithme d'optimisation de programmation des objectifs, destin modliser les conditions des conventions d'exploitation supplmentaires rattaches ce Trait du fleuve Columbia qui lie BC Hydro aux instances tats-uniennes.La technique de programmation des objectifs ("GP") est une mthode de programmation multiobjective dj utilise dans de nombreux domaines, dont l'optimisation d'exploitation de rservoir.Depuis sa prsentation en 1961, on y a eu abondamment recours et on considre qu'il s'agit d'une technique de modlisation solide.L'algorithme de programmation des objectifs que nous avons labor modlise la problmatique multiobjective en misant sur un ensemble de techniques lexicographiques et de programmation des objectifs pondre.Des tudes de cas tenant compte de cinq scnarios ont t ralises afin d'valuer le respect des exigences environnementales correspondant divers dbits viss la frontire canado-amricaine, la hauteur du fleuve Columbia.L'algorithme de GP, nous avons mis au point permet de contraintes s'carter d'une valeur cible.Ceci son tour fournit la flexibilit de modlisation pour traiter infaisabilit rencontre typiquement lorsque des contraintes dures sont incluses dans la formulation du problme d'optimisation.GP peut tre utilise pour tudier les compromis entre plusieurs objectifs en rduisant l'cart par rapport aux niveaux cibles spcifies par l'utilisateur.De plus, la formulation de la programmation des objectifs peut ainsi reflter des conditions d'exploitation en situation relle d'un systme complexe plusieurs rservoirs (comme celui de BC Hydro) plus ralistes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".