Modeling and Impact Analysis for Solar Road Integration in Distribution Networks

Abstract

Solar road (SR), as an emerging generation technic with increasing potential, could save valuable land resources and promote the low-carbon development of both the transport and energy sectors. However, the strong uncertainties of SR may make it challenging for the secure operation of power systems. Thus, this paper aims to investigate the influences of uncertainties of SR on the safe operation of distribution networks. To this end, a novel analytical SR generation model considering the dynamic shadow of vehicles (DSoV) and mixed traffic flow (MTF) is proposed for the first time based on solar geometry and shoelace theorem. The proposed SR generation model can directly reflect the influence of shading effects composed of different types of vehicles on SR generation. The uncertainty of MTF is modeled by improved kernel density estimation (IKDE) and Gaussian mixture model (GMM). Finally, a learning-based probabilistic power flow (PPF) is introduced for the impact assessment of SR integration, while reducing the computational complexity of PPF. Case studies performed on practical power-traffic networks with real historical data corroborate the effectiveness and scalability of the proposed method.