Reference number : GC-2017-Q1-02917, amendment number : , agreement type : G, recipient legal name : Musilek, Petr (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The balance between demand and supply is crucial to the safe, reliable and efficient operation of electric power systems. With the advent of distributed energy resources (such as photovoltaic panels and battery storage systems), maintaining this balance becomes more and more challenging. This is caused by the unprecedented complexity of modern power grids, and by the limited control over the energy produced by intermittent energy sources, such as wind and solar. This research program will address these challenges by exploiting energy system data (including data on generation and loads, weather forecasts and energy market conditions) for the design, monitoring and control of electric power grids. 
 This research will leverage and expand the successful results developed during the previous discovery funding cycle, and under related collaborative R&amp;D projects with industry. It will develop new solutions for managing energy in a variety of contexts, from residential buildings, through community energy storage systems, to grids powering extensive urban and rural areas with a high penetration of dispersed generation and renewable energy sources (such as roof-top solar panels or small wind turbines combined with battery energy storage). 
 The use of a data-driven approach is a distinguishing aspect of this proposal. It means that the safe, reliable and efficient operation of modern electric power systems will be driven by local measurements of how the system is behaving now and has behaved in the past. The data-based predictive energy management methods and agent-based balancing protocols will effectively integrate smart grid components with forecasting, analytic and control functions that will also be developed in this research. This will lead to a new type of distributed, reconfigurable systems capable of local optimization while providing global efficiency and reliability through coordination. In practice, this will mean lower power bills for consumers and improved profitability for energy companies . 
 The developed technology will be essential in realizing the expected environmental, reliability and economic benefits of modern electrical grids. This is crucial as the implementation of renewable generation technologies is expected to double by 2020, also doubling the current Canadian investment in renewables of C$10B per year. In addition to ensuring return on this investment, the research outcomes will also bring more direct economic benefits through commercialization, technology transfer and spin-offs . 
 Over the course of five years, this research program will also train a number of highly qualified professionals (HQPs) including up to 4 PhD and 6 MSc graduates and 10 BSc students. They will be equipped with the expertise to design, plan and operate the future smart power systems, and conduct related advanced research in an academic or industry setting., program purpose french : The balance between demand and supply is crucial to the safe, reliable and efficient operation of electric power systems. With the advent of distributed energy resources (such as photovoltaic panels and battery storage systems), maintaining this balance becomes more and more challenging. This is caused by the unprecedented complexity of modern power grids, and by the limited control over the energy produced by intermittent energy sources, such as wind and solar. This research program will address these challenges by exploiting energy system data (including data on generation and loads, weather forecasts and energy market conditions) for the design, monitoring and control of electric power grids. 
 This research will leverage and expand the successful results developed during the previous discovery funding cycle, and under related collaborative R&amp;D projects with industry. It will develop new solutions for managing energy in a variety of contexts, from residential buildings, through community energy storage systems, to grids powering extensive urban and rural areas with a high penetration of dispersed generation and renewable energy sources (such as roof-top solar panels or small wind turbines combined with battery energy storage). 
 The use of a data-driven approach is a distinguishing aspect of this proposal. It means that the safe, reliable and efficient operation of modern electric power systems will be driven by local measurements of how the system is behaving now and has behaved in the past. The data-based predictive energy management methods and agent-based balancing protocols will effectively integrate smart grid components with forecasting, analytic and control functions that will also be developed in this research. This will lead to a new type of distributed, reconfigurable systems capable of local optimization while providing global efficiency and reliability through coordination. In practice, this will mean lower power bills for consumers and improved profitability for energy companies . 
 The developed technology will be essential in realizing the expected environmental, reliability and economic benefits of modern electrical grids. This is crucial as the implementation of renewable generation technologies is expected to double by 2020, also doubling the current Canadian investment in renewables of C$10B per year. In addition to ensuring return on this investment, the research outcomes will also bring more direct economic benefits through commercialization, technology transfer and spin-offs . 
 Over the course of five years, this research program will also train a number of highly qualified professionals (HQPs) including up to 4 PhD and 6 MSc graduates and 10 BSc students. They will be equipped with the expertise to design, plan and operate the future smart power systems, and conduct related advanced research in an academic or industry setting., agreement title english : Data-driven Methods for Integration of Distributed Energy Resources, agreement title french : Data-driven Methods for Integration of Distributed Energy Resources, agreement number : RGPIN, agreement value : 165000.0, 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