Constructing a Locational Marginal Emission Model Framework for Electrical Distribution Grids: Application to the Province of Alberta Grid for Dual Cost-Emissions Optimization.

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Widespread electrification plays a vital role in Canada's "2030 Emissions Reduction Plan" success. However, for electrification to deliver its potential, it must be followed by a reduction in the electricity sector's greenhouse gas (GHG) emissions to as close to zero as possible while maintaining the stability and reliability of the energy supply. This grant pertains to the latter. Successful GHG reduction in electrical grid operation depends on several factors. Electricity grid operation is a complex combination of heterogenous processes spanning a time frame of years ahead to a few milliseconds. Many stakeholders influence the operation ranging from policymakers to end consumers. While a plethora of prior art can be found discussing the grids' optimal operation, one main challenge remains barely touched; there exists no framework to incorporate real-time marginal emissions of the system into decision-making by stakeholders. Local direct measurements from generators provide no locational marginal (LM) information that is necessary for optimal real-time operation. The emphasis on "real-time" deals with the fast varying operation trajectory of modern electrical grids, caused by high penetration of stochastic agents such as renewable energies and electric transportation. The emphasis on LM is to enable active participation of distributed energy resources and demand to minimize GHG emissions.This grant will propose a framework for modeling and estimating the locational marginal emissions, and apply it to the province of Alberta, Canada. The multidisciplinary research will use machine learning methods to map real-time and day-ahead LM emissions. The framework will be used to develop an optimal dispatch tool for dual cost-emission minimization while considering grid operational constraints. The objective is for distributed energy resources and end users to understand their impact on GHG emissions and to actively participate in emissions reductions. The work will be done in partnership with industry and provides guidance to grid operators for minimizing GHG emissions. Commercialization of the findings is highly anticipated.