Optimal Grid Management for Large-Scale Sustainable Coordination

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Recent changes in climate patterns are causes for great consternation as the ensuing environmental consequences are devastating and costly. Climate change related costs are projected to increase up to $43 billion every year in Canada by 2050. In light of these repercussions, many nations (including Canada) have pledged to actively limit the impact of climate change while increasing resilience to related adverse risks in the Paris Climate Accord established in 2016. One vital component of this initiative is the promotion of clean energy production and consumption.******Mechanisms actively engaged today for reducing greenhouse emissions and carbon footprint are predominantly based on monetary incentives. For example, carbon pricing penalizes generation sources with high carbon emissions and time-of-use pricing assigns fixed prices for electricity consumption over specific blocks of time periods over a day via smart meters. As these do not reflect actual costs and congestions incurred for generation, transmission and distribution; inefficiencies are inevitable. Active mechanisms that optimally coordinate actuating power components in real-time to promote grid sustainability are still not yet a reality due to the intractability caused by the presence of large number of control variables, highly fluctuating non-linear constraints and complex dynamics modelling underlying physical interdependencies.******The proposed research program aims to tap onto the ubiquitous cyber-physical capability present in the modern smart grid to construct a more adaptive and unified grid management framework consisting of three specific objectives: 1) contextual grid monitoring; 2) large-scale coordination of actuating power components residing at the transmission, distribution and microgrid levels; and 3) active planning for increasing system resilience to constantly occurring changes. To achieve these goals, the inherent separability and decomposable structure of optimization and control formulations in the grid will be capitalized to construct adaptive monitoring, control and planning algorithms at various degrees of granularity. Recent advances in large-scale optimization, distributed game theory and social learning analysis will be leveraged to design information exchanges and strategic local decision-making for large populations of power entities to enhance the efficiency and sustainability in power grid operations.******This proposal is in line with the climate change initiative taken at the federal level by the government of Canada via the Pan-Canadian Framework on Clean Growth and Climate Change. Core goals of this initiative include supporting sustainable technology and incorporating resilience to adverse effects of climate change. These are also key elements of the proposed research program contributing to the efforts of securing a safe and sustainable planet for future generations.