Reference number : GC-2017-Q1-01806, amendment number : , agreement type : G, recipient legal name : Ghaddar, Bissan (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Global energy demand has continued to rise since the mid-20th century as a result of industrial development and population growth. The development of renewable energy sources has become a matter of priority to keep up with increasing demand and to limit greenhouse gas emissions. However, the widespread adoption of renewable energy has been limited by its drawbacks, namely, the discontinuity of generation which can lead to demand-supply imbalances and grid congestions. A key strategy to overcome such issues is implementing intelligent management systems and introducing flexibility in the distribution network. Recent developments in a number of technological areas have made important strides toward the realization of a smarter grid. These include a) advanced monitoring, communication and control infrastructure; b) energy storage resources; and c) vehicle-to-grid systems, which represent a promising source of electrical flexibility. This research program will address the last two items by developing mathematical optimization models and algorithms to allow the integration of stationary and mobile energy storage into the energy network, thus enhancing grid reliability and reducing associated uncertainties. 
 
This research advances existing work in several important thrust areas. The developed optimization models will take into account the temporal nature of the problems, the non-linearities due to underlying technical and physical processes, the uncertainties in the load and supply of energy, and the discrete nature of some of the decisions. These mathematical formulations will result in non-linear optimization models that are difficult to solve, and that will require novel solution methods. The proposed holistic integrated models will allow for optimal infrastructure design as well as optimal charging and discharging operations of energy storage systems. 
 
The work described above will contribute both theoretically and practically to several areas of operations research and the development of new models and solution methods for solving sustainable energy problems. The results will be used to provide a plan for scalable integration of intermittent energy using advanced information management and analytics to increase power system flexibility, and balance conventional and renewable energy resources. Through the proposed research program, students will gain comprehensive knowledge and training in the subject areas such as operations research, data analytics, and energy networks, and expertise that will be essential in roles such as data scientist, solution developer, or operations manager to improve Canada&rsquo;s energy network and help Canada increase its uptake of energy generated by renewable resources in the long-term., program purpose french : Global energy demand has continued to rise since the mid-20th century as a result of industrial development and population growth. The development of renewable energy sources has become a matter of priority to keep up with increasing demand and to limit greenhouse gas emissions. However, the widespread adoption of renewable energy has been limited by its drawbacks, namely, the discontinuity of generation which can lead to demand-supply imbalances and grid congestions. A key strategy to overcome such issues is implementing intelligent management systems and introducing flexibility in the distribution network. Recent developments in a number of technological areas have made important strides toward the realization of a smarter grid. These include a) advanced monitoring, communication and control infrastructure; b) energy storage resources; and c) vehicle-to-grid systems, which represent a promising source of electrical flexibility. This research program will address the last two items by developing mathematical optimization models and algorithms to allow the integration of stationary and mobile energy storage into the energy network, thus enhancing grid reliability and reducing associated uncertainties. 
 
This research advances existing work in several important thrust areas. The developed optimization models will take into account the temporal nature of the problems, the non-linearities due to underlying technical and physical processes, the uncertainties in the load and supply of energy, and the discrete nature of some of the decisions. These mathematical formulations will result in non-linear optimization models that are difficult to solve, and that will require novel solution methods. The proposed holistic integrated models will allow for optimal infrastructure design as well as optimal charging and discharging operations of energy storage systems. 
 
The work described above will contribute both theoretically and practically to several areas of operations research and the development of new models and solution methods for solving sustainable energy problems. The results will be used to provide a plan for scalable integration of intermittent energy using advanced information management and analytics to increase power system flexibility, and balance conventional and renewable energy resources. Through the proposed research program, students will gain comprehensive knowledge and training in the subject areas such as operations research, data analytics, and energy networks, and expertise that will be essential in roles such as data scientist, solution developer, or operations manager to improve Canada&rsquo;s energy network and help Canada increase its uptake of energy generated by renewable resources in the long-term., agreement title english : New optimization techniques to integrate storage and renewable energy in the power network, agreement title french : New optimization techniques to integrate storage and renewable energy in the power network, agreement number : RGPIN, agreement value : 110000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada