Optimal Carbon-Aware Operation of Distribution Networks Considering PV Uncertainty via Wasserstein-based Chance-Constrained Programming

This study proposes a carbon-aware power distribution system management algorithm that addresses the uncertainties of photovoltaic (PV) generation output. To model this uncertainty, we adopt a Wasserstein metric-based distributionally robust chance constrained programming (DRCCP) framework, that could estimate the distributional ambiguity using historical data samples. Furthermore, to operate the power distribution system environmentally friendly generating low carbon emissions, we integrate the concept of carbon emission flow into the power management strategy. This concept quantitatively estimates virtual flow and generation of carbon emissions associated with power flows in the power system. Simulation results demonstrate that the proposed algorithm significantly reduces total carbon emission in the distribution system considering the PV uncertainties.