Grant: Collaborative Research and Development Grants
École de technologie supérieure
$98,522.00
Value chain optimization of hydroelectric power generation systems
EF 2019-20
Résumé
Hydroelectric power generation is one of Canada's most important assets, covering over 60% of the country's total electricity demand. In the near future, some of the oil-based demand will transition to electricity due to the mass-marketing of electric vehicles and shift to electric public transit. This will exert supplementary pressure on the electricity demand in Canada. This project aims to identify areas of improvement in the operation of hydroelectric power generation systems and develop tools to measure the effects of proposed improvements. More specifically, this project aims to improve the value chain of the hydroelectric power generation operations. This includes gathering as much information as possible from the short-term and medium-term weather forecasts and observed climatology, transforming that information into the best possible estimates of future inflows to hydropower reservoirs and then making the optimal water releases from the reservoirs. This multi-step process is stochastic in nature and probabilistic approaches will have to be implemented and improved upon to attain the objective. The novelty of this project lies in the skill evaluation of multiple combinations of forecasting tools and quantifying the improvements using a hydropower system simulator. The proposed research and simulation tools will be developed and investigated for a 4-reservoir, 6-generating station hydropower system in central Québec which is operated by Rio Tinto for its aluminium smelting plants. Derivatives of this system will also be independently investigated, such as by adding or removing reservoirs, which will allow generalizing the results to different hydropower arrangements.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
522126-2017
Name nom
Arsenault, Richard
Department département
Génie de la construction
Organization id
53
Institution établissement
École de technologie supérieure
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2017
Award amount
98522
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
405
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Modelling and mathematical simulation of natural processes
Area of application fr
Modélisation et simulation mathématique des processus naturels
Research subject code
4500
Research subject group en
Hydrology
Research subject group fr
Hydrologie
Research subject en
Hydrology
Research subject fr
Hydrologie
Installment
inst:2 - 2
Partie
2
Nb partie
2
Application title
Value chain optimization of hydroelectric power generation systems
Keyword
NSERC
Application summary
Hydroelectric power generation is one of Canada's most important assets, covering over 60% of the country's total electricity demand. In the near future, some of the oil-based demand will transition to electricity due to the mass-marketing of electric vehicles and shift to electric public transit. This will exert supplementary pressure on the electricity demand in Canada. This project aims to identify areas of improvement in the operation of hydroelectric power generation systems and develop tools to measure the effects of proposed improvements. More specifically, this project aims to improve the value chain of the hydroelectric power generation operations. This includes gathering as much information as possible from the short-term and medium-term weather forecasts and observed climatology, transforming that information into the best possible estimates of future inflows to hydropower reservoirs and then making the optimal water releases from the reservoirs. This multi-step process is stochastic in nature and probabilistic approaches will have to be implemented and improved upon to attain the objective. The novelty of this project lies in the skill evaluation of multiple combinations of forecasting tools and quantifying the improvements using a hydropower system simulator. The proposed research and simulation tools will be developed and investigated for a 4-reservoir, 6-generating station hydropower system in central Québec which is operated by Rio Tinto for its aluminium smelting plants. Derivatives of this system will also be independently investigated, such as by adding or removing reservoirs, which will allow generalizing the results to different hydropower arrangements.