4.6 LESSONS LEARNT

Several recommendations for calculating the hosting capacity are extracted from the national and international projects, which are listed as below: Impact factors Based on EPRI [13], [14], following key recommendations on impact factors can be considered while assessing hosting capacity: • DER impacts should be considered to design and study the realistic, worst-case conditions to ensure adverse impacts to reliability. • The hosting capacity should consider the worst-case and best-case conditions to understand the upper and lower limits that should drive decision making. • Considerable work and research are needed to evolve the data requirements, methods for assessment, and tools to evaluate probabilistic (risk-based) methods to enable better quantification of the hosting capacity upper and lower boundary conditions and evaluate the risk of such conditions. • EPRI recommends carefully considering impact factors while deriving practical applications for the results of a hosting capacity study. • It is also critical to understand how those impact factors are, or could be, considered in the applied hosting capacity method.

Optimal mix of data and models

Assumptions around voltage regulation, future load profiles, DER profiles and characteristics, phasing, etc. are necessary due to the uncertainties in underlying data, and 100% accurate hosting capacity is not feasible. However, assumptions and understanding their implications can result in a precious outcome. • Model-based approach The recommendation of this approach based on the project that carried out by the University of Melbourne [17] are as follows: o HV Feeder Selection. DNSPs can reduce the modelling efforts, and time by selecting the HV feeders properly (significantly affecting the hosting capacity assessment). This selection should consider several characteristics such as feeder type (i.e., rural, urban, etc.), topology, length, number of customers, number of installed DERs, etc. Here, applying extreme cases aims to investigate a potential solution's viability; thus, that solution is likely applicable (and might perform even better) in milder cases. o Explicitly Model LV Feeders. DNSPs should consider the integrated HV-LV feeder models with detailed modelling for the LV feeders down to household connection points in calculating hosting capacity. This is essential to fully consider the voltage-related control actions from the controllable devices to quantify voltage issues and their effects on hosting capacity assessment. o Cater for Uncertainties. DNSPs should consider the uncertainties related to future DERs location, size, and meteorological and demand profiles to the extent that is possible. • Smart meter-driven approach The recommendation of this approach based on the project that carried out by the University of Melbourne [17] are as follows:

o Run Trials on Actual Distribution Transformers. DNSPs with available smart meter data should run trials of the smart-meter driven approach and compare the results with their existing DER hosting capacity assessments. o Use Data that Covers a Minimum DER Penetration Increase. For DNSPs to successfully use the proposed smart meter-driven approach, it is recommended to use the historical data that cover a period where a minimum increase of DER penetration occurs, i.e., 30% for distribution transformers in urban HV feeders and 20% for those in rural HV feeders. o Other Considerations. Changes is the voltage level due to the tap changing in the zone substation’s OLTC actions can slightly reduce the accuracy of the DER hosting capacity estimations. Furthermore, the accuracy of the prediction limits also reduces because of the

OLTC actions that can create voltage spikes and become outliers.

Analysis framework

DNSPs should carry out time-series simulations to reflect the time-dependent aspects of demand/generation and controllable devices in calculating hosting capacity. This enables a more accurate assessment of voltages and power flows regarding time variation and control device actions to support realistic dynamic “boundary conditions” [19]–[21].

Hosting capacity calculation methods

Based on EPRI [14], [31], the recommendations for each hosting capacity calculation method are summarised as below:

• Stochastic Method o This method does not guide DER developers or DNSPs engineers on location specific DER impacts. o The detailed implementation of this method is not easily repeated or replicated to entire distribution systems, and the analysis can take hours to days to evaluate a single feeder. o This method can be very effective for research purposes, but it is not recommended for any extended application exceeding that. o The method is implemented uniquely for individual distribution planning tools. • Streamlined Method o Developing the agnostic (non-specific DER) hosting capacity is a unique aspect of the approach that enables the rigorous hosting capacity assessments to be performed upfront while allowing the actual DER-specific results to be derived offline, but the process needs further validation. The streamlined method results should be compared with results from other methods to gain confidence in the accuracy. o The streamlined approach can be implemented in multiple time periods to derive a timebased hosting capacity. o Further consideration should be given to the impact of the input load forecasts as slight variations in the shape of the load (and existing DER) forecast can significantly impact DERspecific hosting capacities. o The method can be implemented independent of distribution planning tools. • Iterative Method o The simulation time to perform the iterative analysis increases with the feeder numbers and additional impact factors. o Limiting the cases, locations, and scenarios considered or making the analysis more efficient can reduce the computational burden.