Enhancing hosting capacity for renewable generation projects: a comparative study of quasi-dynamic and static simulations in planning network analyses

Planning is essential for electric utilities when connecting new generation projects to distribution networks, especially as the demand for renewable energy sources like wind and solar grows. Utilities must ensure these connections are reliable and efficient. One common planning method, currently utilised by the Irish DSO, is the "full wind, full solar, minimum demand" scenario, which models the grid assuming wind and solar output are at their peak and demand is at its lowest. While this ensures the grid can handle worst-case scenarios, it often results in overestimated capacity needs, potentially leading to higher connection costs and underutilized infrastructure. A quasi-dynamic approach, modelling the grid using historical data and renewable generation patterns at hourly intervals, may deliver a clearer picture of the grid’s utilisation, and lead to more accurate and resource-efficient planning. In this paper, we compare the method currently used by the Irish DSO with our proposed quasi-dynamic analysis. Our proposed analysis consists of 8760 power flow simulations; forecasted generation patterns and relevant load data are inputted into a Python script that automates data collection and processing with PowerFactory. The results are collected and analysed, and visualization tools aid presentation of results, indicating whether the grid operates within planning standards.