Optimization of smart net-zero energy communities

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Net-zero energy (NZE) communities generate as much renewable energy as they consume over a typical meteorological year. This is a significant departure from the present building stock which is estimated to use 31% of primary energy and about 50% of total electricity produced in Canada. NZE communities satisfy a strategic need to reduce energy use intensity, integrate clean energy supplies and mitigate environmental impacts of the new and existing building stock, all while meeting expectations for human well-being and economic growth with a measurable goal. This research program proposes three strategic areas to facilitate the engineering design of cost-effective NZE community archetypes for Canadian climates. Archetypes will be sufficiently robust to provide essential services during grid-outages and extreme weather events and may significantly reduce initial engineering and design costs of future community developments. The scientific approach combines building performance simulation, optimization algorithms, energy storage and machine learning. Computer models enable designers and developers to explore a plethora of energy saving strategies using economic and energy performance metrics before construction. From this, solutions which best meet their particular design goals can be identified and further refined. In this proposal, building performance is quantified using life-cycle cost, carbon and net-energy consumption. The scientific contributions include the development, documentation and validation of modelling methodologies which encapsulate the energy and economic savings of net-zero energy communities located in key Canadian climates. The novelty of the methodology is its reproducibility for industry use, focus on robust archetypal community designs, and holistic appraisal of both building and district systems using energy, carbon and economic indicators. The research program advances NZE community design and operational knowledge by proposing integrative modelling approaches with the goal of finding optimal trade-offs between building and district energy systems. In addition, the research explores socio-economic factors and may aid in informing future energy policy and development of effective incentive programs to further advance NZE building and community adoption.