Learn how we leveraged data-intensive analytics for optimal integration of renewable Distributed Generation (DG) and storage in distribution networks for optimized Energy Community (EC) planning, sizing and operation. Energy Communities aim to foster Distributed Generation by supporting generation and simultaneous consumption at the local level to avoid impacts on the distribution (and even transmission) network. Current regulation requires EC to be bounded to the grid portion fed by a single primary substation, which can be a pretty large portion of the network with several different constraints (cable sizing, load distribution, etc.) in different areas. We wanted to identify analytic methods to specify the maximum DG hosting capacity of the different portions of the network, considering hardware constraints, load models and DG installation forecast at a fine grain level in the network. We also wanted to characterize distribution network voltages without electrical models. The growing uptake of PV forces distribution companies to estimate network voltage rise issues for operation and planning. Voltage calculations are based on power flow analyses and require detailed, complex three-phase electrical models, which are not readily available for most distribution companies. We focus on capturing the nonlinear relationships among the historical data (demand and voltages) and the corresponding feeders.
