Component Level Analysis and System Optimization for a Net Positive Energy Infrastructure

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Utilizing renewable energy such as solar, wind, biomass and geothermal to power an infrastructure requires a design tool which allows, based on the given geological and energy demand data, a flexibility in choosing among renewable energy sources and associated energy conversion and storage technologies. Changfeng Energy Inc., a Canadian public company, has been actively exploring emerging-market opportunities in carbon neutral and net positive carbon footprint infrastructures. The company has identified two major urgent RD needs: (i) to pursue theoretical/numerical design and analysis of an energy system (and its components) using an existing dispatching strategy and (ii) to carry on assessment and optimization of a practical system via implementation of novel control algorithms. This CRD project, led by the University of Waterloo, will directly address the first task through investigating the feasibility, boundary and performance of a renewable based energy network for a sophisticated infrastructure. A recently developed smart energy network (SEN) concept will be implemented and its functions will be validated using an existing infrastructure EVOLV1 which was designed to achieve the net positive carbon footprint. This project will address the second task through simulating the operation and performance of the proposed SEN system, using the real-life operation data from EVOLV1 which is equipped with solar, geothermal, and electric vehicle charging. Biomass gasification will be added, in order to increase the thermal energy use from the waste. Rule based and machine learning (more specifically, Reinforcement Learning or RL) control strategies will be applied to improve both energy and environmental performances. The overall objective of this CRD project is to develop a design tool for the industry partner to design carbon neutral and net positive buildings. Important outcomes of this project include (i) critical considerations and indicators for achieving net positive carbon footprint design, (ii) sizing and modeling methods for energy management; and (iii) an optimal control algorithm and hardware implementation for the SEN-based energy system.