With the increasing adoption of distributed energy resources (DERs) such as wind and solar photovoltaics (PV), many distribution networks have changed from passive to active. In turn, this has led to increased technical and operational challenges such as voltage issues and thermal loading in high DER penetration scenarios. These challenges have been further increased by the uncertainties arising from DER allocation. The implication of DER allocation uncertainty in the planning process is far-reaching as it affects critical planning processes, including conductor size selection (CSS). Most reported CSS methods in the literature do not include DER allocation uncertainty modeling as they are mostly deterministic and are set out as optimization problems. The methods, therefore, lack foresight on future loading conditions and cannot be used in a CSS process for feeders with high DER penetration. This paper proposes a novel input–process–output stochastic–probabilistic CSS framework for distribution feeders with DERs. The efficacy of the proposed framework is demonstrated using a low voltage feeder design case study with varying PV penetration targets, and the performance compared to deterministic–active-based estimates from our earlier work. The proposed CSS method is well-suited to the sizing of conductors for future loading conditions considering DER allocation uncertainty and will therefore be useful to planners working on new electrification projects.
Globally, solar photovoltaic (PV) installations on distribution LV feeders have increased significantly. The increased penetration leads to several technical problems on existing networks and impacts utilities' business models as energy sales drop. For proactive management of these challenges, utilities need to continually monitor the capacity of installed PV. To this end, some utilities typically require PV installations to be registered and sometimes use GIS mapping to approximate the installed PV capacity. However, these GIS PV capacity estimations methods are unreliable. Therefore, to obtain reliable PV capacity estimates at a distribution level, comprehensive modeling is required to accurately represent the generation output and the distribution load. This paper proposes a novel probabilistic PV estimation method that uses time-series historical sets of load and irradiance data to estimate the embedded PV capacity while considering the uncertainty in solar irradiance and the measured net load. Uncertainty characterization is implemented using empirical probability density functions, and simulation is performed stochastically using Monte-Carlo methods. A novel quantile analysis approach is developed and used in the computation of the final PV estimates. The proposed methodology is tested using measured load data from Ausgrid customers, Australia, and achieves reasonable accuracy (between 86% and 90% for the tested cases) and a 10% mean absolute percentage error. This approach is robust to the effects of input uncertainty and can be used by distribution utilities to estimate and monitor PV capacity installed on the distribution networks without incurring extra advanced metering investment costs.
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