Capacity Constraint-Based Method Capacity constraint-based methods utilise power flow analysis capabilities to approximate the set of DER, and other network loads and generation units, operational set points that do not violate a defined list of technical and operational constraints. These Capacity Constraints are typically represented as a list of inequality constraints on the decision variables that can be used in standard optimisation frameworks or analysed using geometric approaches. In most cases these Capacity Constraint analysis is performed considering a base case load flow which can be derived from forecasts or from State Estimation systems. This type of methods decouples the optimisation (e.g. a capacity allocation) problem from the underlying power flow problem and is standard practice in most electricity markets, including the National Electricity Market.
Supervised univariate regression model (SURM) The obtained new dataset containing maximum voltage and aggregated power is used to train a supervised (i.e., gradient decent) univariate regression model that represents the DER hosting capacity estimation model for the analysed LV network. As a first step to develop this model, all the customers’ daily smart meter data in a LV network are collected by leveraging the smart meter database. In the next step, smart meter data are analysed and cleaned from missing and inconsistent values. A new and clean dataset is obtained which contains the maximum voltage and the corresponding aggregated power for each day. The main idea of reviewing the above-mentioned hosting capacity assessment methods is to show the accuracy of each method. The question is which method should be used when, and which method is the most applicable. Choosing a hosting capacity method heavily depends on the objective of a particular study. A comparison has been carried out in California [23] as part of the Demo projects to evaluate the accuracies of these methods. It is noted that the iterative method produces accurate results. However, it is not necessarily true that the iterative method should be used as a benchmark or reference for other calculation methods. The accuracy of hosting capacity assessment methods should be compared based on the results that can be obtained considering all the impact factors, regardless of the method. The impact factors can affect the accuracy of the hosting capacity results. Most projects that calculate hosting capacity in Australia do not clearly mention the use of any of the EPRI recommended hosting capacity calculation methods. However, the projects that used PowerFactory and OpenDSS software utilised DRIVE method. In addition, capacity-constraint based methods are used to approximate the hosting capacity in three national projects [11,21,22]. Also, a new hosting capacity calculation method has been proposed in [17], which is called “supervised univariate regression model”. This method is carried out based on a smart meter-driven approach. This framework required a high penetration of smart meters data. Further investigation needs to be carried out in this context to validate EPRI and the University of Melbourne recommended hosting capacity calculation methods for several use cases and based on their significant impact factors.
4.2.5 Simulation Tools Several simulation tools are available in the market to analyse the electrical distribution network. The tools can conduct balanced and unbalanced power flow and balanced and unbalanced state estimation under fundamental frequency and harmonics cases. Some software tools are capable of 51
