A Probabilistic Conductor Size Selection Framework for Active Distribution Networks

Lewis Waswa, Munyaradzi Justice Chihota and Bernard Bekker
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Lewis Waswa: Department of Electrical & Electronic Engineering, Stellenbosch University, Stellenbosch 7602, South Africa
Munyaradzi Justice Chihota: Department of Electrical & Electronic Engineering, Stellenbosch University, Stellenbosch 7602, South Africa
Bernard Bekker: Department of Electrical & Electronic Engineering, Stellenbosch University, Stellenbosch 7602, South Africa

Energies, 2021, vol. 14, issue 19, 1-19

Abstract: 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.

Keywords: distributed energy resources; distributed generation; hosting capacity; Monte Carlo simulation; after diversity maximum demand; probabilistic methods (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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