Development of computational tools for designing water and energy nexus: Novel applications of multidisciplinary optimization and machine intelligence

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Investing in a large scale development of highly distributed sustainable energy can simultaneously solve our energy problem, our climate problem (in terms of reducing our impacts on changing climate) while giving employment to a large number of people that help improve the economy while improving equity. Today, sustainable (renewable) energy comes mainly from hydropower, wind and solar energy, and biofuels. These resources depend on water and land. Most tools that were developed in the past for designing these systems didn't take into consideration global climate (and changes), hydrology and local geography (e.g. the size of Ontario), uncertainty in energy production, and local/global feedbacks. Such objectives call for the development of methods supported by mathematical models and numerical solutions using Big data on meteorology, hydrology, geography, and societal requirements and these fall in the context of the water--energy nexus. Solving these problems efficiently requires integrated modeling tools together such as surrogate optimization for computational efficiency, which may involve machine intelligence -based function approximation of costly simulation models, agent-based modelling for simulating diverse human activities, and both gradient--based and gradient--free optimization algorithms. The applicant's expertise in these methods will be combined to help solve sustainable energy problem for now and the future. Sustainability demands long-term global outlook. Additionally, practical social requirements demand consideration of local short-term costs and benefits. Ontario, a large province both in population and size in Canada is heavily dependent (over 60% of the total energy generated) on nuclear energy for its noncarbon based energy generation. The future of nuclear is unclear, however, Ontario also has huge potential for renewable energy and this proposal aims to fill the gap that exist in modelling and design methodologies required for large scale implementation of renewable energy including pumped storage hydropower systems (cheaper alternatives to batteries for storage but available only in specific locations) to enhance the intermittent generation of renewable energy. Renewable energy is highly dependent on environment as its source, but non-renewable energy is highly impactful on the environment. Specialized models and methods are necessary to consider both the diffusive and the concentrated nature of water and energy. We will develop methods that can simultaneously consider human uses of energy and water guided by sustainability as the goal to map out large potential areas for such development. Methods are extendible to other provinces for renewable energy development but also for other large scale problems in the environment.