factors. The range of applied impact factors can change depending on the considered feeder metrics. Hosting capacity can be assessed for each of these feeder metrics independently to provide visibility into each metric contribution. Most important in applying the results from a hosting capacity study is to access each metric's hosting capacity. The feeder metrics mapped to the impact factors to determine hosting capacity boundary conditions recommended by EPRI [13] are presented in Table A 2-1 in the Appendix. The accuracy of a hosting capacity calculation increases with the number of impact factors considered. However, consideration of multiple impact factors increases the computational burden and complexity in assessing hosting capacity. Utilizing particular grid and DER assumptions, boundary cases can be identified that calculate the minimum and maximum hosting capacities without needing the execution of repetitive cases. Identifying the lowest (worst-case) and highest (best-case) hosting capacities is essential for assisting with interconnection requests and informing DER installers and project developers. A summary of the impact factors, and their relative ranking as recommended by EPRI [14] is provided in Table 2-1 in Appendix 2. In Australia, Energeia [15] recommended several impact factors and their related metrics to calculate the hosting capacity as (i) power quality (i.e., over-voltage, under-voltage, flicker, and total harmonic distortion), (ii) reliability (i.e., thermal overload, safety protection, mal-operation, and islanding), (iii) system security (i.e., disturbance ride-through, and under frequency load shedding), and (iv) cost/efficiency (i.e., phase imbalance, and forecasting error). The effect of miscoordination of protection devices on hosting capacity has not been analysed thoroughly. However, based on the reviewed national projects, it noticed that some or all of the following impact factors are used to calculate the hosting capacity such as configuration, voltage regulation, thermal limits, connected load, connected DER, control-managed, DER portfolio, DER location-site specific, and/or time (see Table 4-4-4). In addition, Horizon Power in [16] used more specific impact factors, such as the output fluctuation factor, diversity factor, and other appropriate factors for time intervals, to determine the hosting capacity.
4.2.2 Optimal mix of data and models To perform hosting capacity across an entire LV distribution network system, a large amount of validated data is required. Missing and/or inaccurate grid data is a significant concern to utilities undertaking hosting capacity analyses. Unlike some conventional planning studies, hosting capacity assessment and the interconnection review process requires a higher degree of grid model accuracy in connectivity information and electrical parameter data. Besides, rectifying data quality and completeness issues can be highly time-consuming and expensive for DNSPs. Capturing the needed datasets for network segments composed of hundreds to thousands of customers, devices, and characteristics that are dynamic in time is not trivial. There is a need to validate existing data and gather new, previously untracked data based on the significant impact factors for a use case. Multiple source systems like Geographic Information Systems (GIS), Customer Information Systems (CIS), Asset Management Systems, and engineering design tools are involved in data collection and gathering processes. A wide range of techniques is employed, including correlating information from disparate repositories, searching paper records, leveraging validation algorithms, and performing extensive field surveys. After gathering and validating data, one of the critical challenges is to convert data to the planning tool format while ensuring consistency and accuracy of data. A certain level of manual support is needed to complete this process. Achieving a grid model with the requisite level of data integrity and completeness is often a multi-year, multi-tensof-million-dollar process. 46
