The increased connection of distributed generation (DG), such as photovoltaic (PV) and wind turbine (WT), has shifted the current distribution networks from being passive (consuming energy) into active (consuming/producing energy). However, there is still no consensus about how to determine the maximum amount of DGs that are allowed to be connected, i.e., how to quantify a so-called 'hosting capacity' (HC). Therefore, this paper proposes a novel risk assessment tool for estimating network HC by considering uncertainties associated with PV, WT, and loads. This evaluation is performed using the likelihood approximation approach. The paper, also, proposes a utilization of clearness index for localized solar irradiance prediction of PV. In addition, we propose the use of sparse grid technique as an effective means for uncertainty computation while the use of Monte Carlo technique is taken for a comparison purpose. Two actual distribution networks (11-buses and South Australian large feeder) are considered as case studies to demonstrate the usefulness of the proposed tool.
- Active distribution networks (ADNs)
- likelihood approximation approach
- probabilistic hosting capacity (PHC)
- risk assessment
- sparse grid
- uncertainty computation