Abstract
South Africa is experiencing energy outages in the form of load shedding and load reduction due to Eskom demand exceeding the supply and ageing transmission and generation infrastructure. The impact of load shedding has led customers (residential, commercial, and industrial customers) to consider and use alternative energy sources to minimize the impact of load shedding. The research is based on a case study for Greater Tzaneen Local Municipality (GTLM) network. The study outlines the impact of increasing Small Scale Embedded Generators (SSEG) on distribution network voltage regulation, Power Factor (PF), and thermal loading.
The research process begins with analysing the GTLM 66/33/11kV network configuration using Single Line Diagrams (SLD), loadings and operation. The network comprises of multiple main substations (66/33kV) supplying downstream network (33/11kV network) which consists of Medium Voltage (MV), miniature substations and transformers and Eskom bulk supply points. Furthermore, the backbone conductors for the network are Bear and Hare overhead lines. The network loading data for 1 year 4 months from statistics meters is analysed using Microsoft Excel to determine maximum and minimum load conditions of the network. The list of existing registered SSEGs was provided by the municipality while the unregistered SSEGs were identified through Google Earth. Additionally, existing network modelling is performed using PowerFactory DIgSILENT software, with the inclusion of both registered and unregistered SSEGs. Two scenarios (scenario 1 which is maximum loading and scenario 2 which is minimum loading) are modelled, simulated, and analysed using PowerFactory DIgSILENT software. The analysis was done for base case penetration and 15% of MV peak loads penetration (as per NRS097-2-3 guidelines). For both scenarios, the load flow analysis is conducted to assess feeder loadings, busbar voltages, and PF under different load conditions.
The findings revealed that increasing SSEG penetration had a positive impact as it reduces thermal loadings which leads to reduced real and reactive power losses under maximum and minimum loading conditions. However, this increase in SSEG also highlighted challenges of PF (reduced PF) in certain substations, and voltage violations.
To address these challenges, network optimization measures were proposed, including the installation of parallel bear conductors on overloaded feeders, the addition of a shunt capacitor at a specific substation, and changing tap positions on transformers. The estimated project cost for these optimization measures was provided, with a payback period of approximately 15 years. The life span of power systems equipment is 25 years. In conclusion, the study highlights the need for network optimization and investment to accommodate the increasing penetration of SSEGs. While initial capital expenditure is required, the long-term benefits in terms of reduced energy losses and improved network performance justify these measures. This optimization ensures a reliable and efficient power distribution network that can adapt to the evolving energy generation trends, ultimately benefiting both the utility and its customers.