Stochastic Carbon Footprint Tracing Methods in Power Systems

TL;DR

This paper introduces two stochastic methods for more accurate power system carbon footprint tracking that account for renewable energy uncertainties, improving spatial analysis and computational efficiency in large systems.

Contribution

It proposes novel stochastic carbon footprint tracking methods that incorporate RES uncertainty, addressing limitations of existing deterministic approaches.

Findings

The methods effectively model RES uncertainty impacts.

The second method improves computational efficiency for large systems.

Case study demonstrates accurate and efficient tracking in a 1004-bus system.

Abstract

As the penetration of distributed energy resources (DER) and renewable energy sources (RES) increases, carbon footprint tracking requires more granular analysis results. Existing carbon footprint tracking methods focus on deterministic steady-state analysis where the high uncertainties of RES cannot be considered. Considering the deficiency of the existing deterministic method, this paper proposes two stochastic carbon footprint tracking methods to cope with the impact of RES uncertainty on load-side carbon footprint tracing. The first method introduces probabilistic analysis in the framework of carbon emissions flow (CEF) to provide a global reference for the spatial characteristic of the power system component carbon intensity distribution. Considering that the CEF network expands with the increasing penetration of DERs, the second method can effectively improve the computational efficiency over the first method while ensuring the computational accuracy on the large power systems. These proposed models are tested and compared in a synthetic 1004-bus test system in the case study to demonstrate the performance of the two proposed methods