Power Loss Mitigation Using Distributed Generation in Asaba Distribution Network

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

The increasing demand for electricity, coupled with rising economic and environmental concerns, has made renewable energy, particularly solar energy, a major area of interest. This research work evaluates the impact of distributed generation (DG) and presents a methodology for the optimal allocation and sizing of distributed generation within a distribution network in order to minimise power losses and improve voltage profiles. The study involved load flow analysis of the existing 15MVA, 33/11kV Benin Electricity Distribution Company (BEDC) Asaba injection substation distribution network and its 11kV radial feeders (SPC and Anwai Road), connected to an aggregate of ninety-six (96) 11/0.415kV secondary distribution transformers serving as load buses. Simulation, analysis, and integration of photovoltaic (PV) distributed generation into the Asaba injection substation distribution network were carried out using the Newton–Raphson algorithm and the Loss Sensitivity Factor (LSF) algorithm to determine the optimal location and size of the DG units. The network was modelled in ETAP version 12.6 using a detailed single-line diagram developed in the ETAP editing environment. The results revealed that, prior to DG installation, only ten (10) out of the ninety-six (96) buses operated within the statutory voltage limits of 394.25V–435.75V (0.95–1.05 p.u.), while eighty-six (86) buses violated the permissible voltage range. This indicated that the network was weak, unstable, and characterised by high active and reactive power losses of 1329.08kW and 2031.16kVAr, respectively. Following the optimal placement of DG units, active and reactive power losses were reduced by 57.5% and 70.7%, respectively. In addition, ninety-one (91) out of the ninety-six (96) buses operated within the statutory voltage limits. This improvement significantly enhanced the capacity, reliability, and operational efficiency of the distribution network.

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