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Assessment Evaluation of Nigeria’s 330kV Electric Power System Reliability Indices for Benchmarking and Operational Improvement Planning

Abstract

This work determines the reliability indices of the Nigeria 330kV electric power network, which is susceptible to disturbances. Besides, the network configuration is inadequate as it is vulnerable, resulting in transmission line outages. The cardinal object, therefore, is to benchmark the indices against established standards to enable effective operational improvement planning. First, a simulation was conducted using the Electrical Transient Analyzer Program (ETAP) and validated with the Power System Simulator for Engineering (PSS/E) software to assess bus voltages, line flows, and system losses. Subsequently, the ETAP software was applied to determine reliability indices such as the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), Average Energy Not Supplied (AENS) and Expected Energy Not Supplied (EENS). The simulation results obtained for SAIFI, SAIDI, CAIDI, and ASAI on the test network are 3.2684 f/customer. yr, 9.4140 hours per customer in a year, 2.880 hours per customer interruption, and 0.9989 respectively. Likewise, the AENS with gave a high value of 1360.9340 MWh/customer. yr indicating that on the average, customer is are expected to lose access to 1360.9340 MWh of energy annually. Furthermore, the high value of EENS estimated at 55,798.300 MWh/yr means that the power system is expected to fail to supply 55,798.300 MWh of electricity in one year due to various incidents of failure. These values were compared with the standard IEEE values and were found to be outside the threshold; thus, making it imperative that the indices be utilized to undertake further work that would result in improved and efficient operation of the national grid.

Research topics

  • Power System Reliability and Maintenance
  • Power System Optimization and Stability
  • Optimal Power Flow Distribution

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DOI: 10.4028/p-m9mnbs

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