CO₂ Footprint and Nodal Marginal Emission up to Distribution End Users for Energy Mix

Abstract

To achieve the clean energy target set by various countries and states for 2030, it is crucial to assess Carbon Emission Flow (CEF) accurately across the entire energy supply chain. This paper introduces an innovative approach employing artificial intelligence (AI) techniques to calculate both direct (Scope 1) and indirect (Scope 2) CEF, encompassing power generation, transmission, and end-user consumption of radial and loop networks. While direct emissions from connected generators can be measured using power generation data, heat rates, and fuel types, indirect emissions from imported/exported power require AI algorithms analyzing historical and real-time data. CEF for a customer or any entity is the amount of CO2 generated from power sources for that customer or entity. Our methodology computes the total (both direct and indirect) and locational marginal emission (LME) for diverse energy mixes at each node and load in the power network. This approach provides precise location specific CEF data, allowing identification of nodes with varying carbon intensity. By understanding the emissions landscape, policymakers can develop targeted strategies, facilitating the reduction of carbon emissions and guiding future taxation policies. This innovative AI-driven framework enhances our ability to transition towards a sustainable energy future while meeting clean energy goals.

Keywords

• CO₂ flow
• scope 1 and 2 emissions
• heat rate
• energy mix
• distributed energy resources
• locational marginal emission