4.2.3 Analysis framework

require power flow simulations, only historical smart meter data. • It can save time and effort in the assessment of new connection requests.

4.2.3 Analysis framework

Hosting capacity can be assessed on two main analysis frameworks based on the availability of the data and the optimal mix of data and models, namely steady-state analysis, and time-series analysis [18]. The steady-state analysis for hosting capacity assessment is conducted for two base-feeder load levels: the daytime minimum and daytime maximum load, while absolute maximum and absolute minimum loads can be considered if the daytime load levels are not known. These load levels are utilised to derive a bounding worst-case response for extreme conditions for the DER varying from zero to full output. The steady-state analysis procedure involves: (i) solve the power flow with DER output set to zero, (ii) lock all regulator and capacitor switches at their present state, and (iii) solve the power flow with DER producing maximum power. The time-series analysis is conducted for demand/generation coincident scenarios based on time-ofday. Basically, a day of time dependent load can be selected for the maximum load and a day for the minimum load. Each of these days should be evaluated by considering highly variable and non-variable DER output. The load and DER resemble scenarios can be selected from the steady-state analysis results based on monitoring criteria impact. The main objectives of the time series analysis are: (i) determine feeder response from actual load and DER data, (ii) compare time-series response to that indicated with the steady-state analysis, and (iii) determine DER influence on control elements.

An unbalanced three-phase power flow simulation should be run considering changing the demand and generation profiles within a time (i.e., hourly or less) and across various seasons. Therefore, time-series simulation can be carried out to consider the time variations of the profiles based on time-series data (i.e., load profiles, DER profiles, meteorological variables, etc.) with adequate granularity. Determining the adequacy of data granularity (i.e., at least 30-minute resolution) depends on a trade-off between capturing time-dependent aspects, the corresponding computation time, and available historical data [19]–[21]. In practice, the minimum resolution can be defined based on the available timeframe of the data from historical smart meter data, SCADA measurements, and meteorological data.

To calculate hosting capacity based on a model-based approach, the planning standards and guidelines for a distribution network should be defined (i.e., voltage limits, rated capacity of assets, etc.). Then, power flow and/or state estimation can be conducted by including the demand/DERs scenarios of interest. Here, hosting capacity should be calculated by keeping some critical parameters (i.e., voltages, thermal limits, etc.) within pre-specified limits. Power flow aims to obtain the voltage magnitudes and angles at all nodes, and the currents flow in all branches by giving the topology and components of the network and set points of connected loads and generators. Here, unbalanced power flow is the basis of distribution network analysis. Typically, the power flow can be represented by bus injection models or DistFlow equations [21]. State estimation (SE) is a decision support tool that uses the network state information for betterinformed decision processes and the development and implementation of active control strategies.