Abstract
South African energy system is currently transitioning from centralized to decentralized energy systems. This change is caused by several factors such as exorbitant electricity tariffs, not enough electricity to meet the demand, introduction of renewable energy to the grid and energy efficiency initiatives. Energy modelling is the key towards transitioning, as it provides guidance in terms of how much additional capacity is required to meet the electricity demand of the country, a region, or a business. Residential, commercial, and industrial customers have embarked on a journey to introduce renewable energy technologies to reduce reliance on the main electricity grid in South Africa. This dissertation is structured to answer the research question; “What is the least cost electricity supply technology mix for the Council for Scientific and Industrial Research (CSIR) main campus up to 2040?”.
An optimisation bottom-up electricity modelling approach was applied, taking into consideration the electricity demand, current installed technology, new technology options, and operating (OPEX and CAPEX) cost and the technology learning rates. The chosen modelling tool for the dissertation was PLEXOS tool. A top-down method was applied to determine two demand profiles for CSIR system, with 2018 as a baseline year. First demand assumptions was the 2% annual demand reduction from 2019 to 2030, and then a constant demand till 2040. The reduction was assumed based on Energy Efficiency (EE) intervention and Demand Side Management (DSM). The second demand assumption took into consideration the COVID-19 impact at the CSIR, where the demand was half from 2020 to 2023 and then the demand went back to the same demand in 2019 to 2040, considering that no EE and DSM intervention took place.
Two scenarios were analysed, Business as Usual (BAU) scenario which, considers the existing supply options to the CSIR, where the City of Tshwane (CoT) supply more than 80% of the electricity. No additional capacity investment was included on this scenario for the energy planning time horizon from 2018 to 2040. Least cost (LC) scenario, the BAU scenario assumptions were applied on this scenario, however new supply options were added. The additional capacity investment options that can be installed at the CSIR Pretoria campus were solar photovoltaic, biogas and wind.
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The least cost scenario results indicated that both wind and biogas were proven not to be financially viable for the CSIR to implement. The technology which was chosen to be financially viable for the CSIR was to add more rooftop solar PV to meet 50% of the CSIR energy demand. This will enable the CSIR to spend less, as the average cost of electricity is less than the average cost of electricity for BAU. However, the system indicates that it is not financially viable for the CSIR to be off grid.
The average electricity cost is expected to increase in the future. This is due to the annual electricity tariff imposed by the CoT on the first of July each year. The BAU scenario average electricity cost results in 2040 is estimated to be 1100 R/MWh, while the least cost scenario average cost is estimated to be 700 R/MWh. The least cost scenario average cost is low due to the new additional generators from the solar photovoltaic technology, which has a low CAPEX and OPEX compared to other generators.
The research question, “What is the least cost electricity supply technology mix for CSIR main campus in the year 2040?” has been answered. The results indicated that the combination of electricity from City of Tshwane municipality and solar photovoltaic technologies are the least cost energy mix for the CSIR. The 50% electricity contribution from City of Tshwane municipality and 50% of solar photovoltaic technology is required to provide the least cost electricity to CSIR by the year 2040. In conclusion, an additional 2MW of photovoltaic technology capacity is needed to serve the least cost electricity to the CSIR by the year 2040.
A technical conference paper disseminating the results obtained from this research was accepted for 2023 South African Universities Power Engineering Conference (SAUPEC) and was presented on the 25th of January 2023 at the University of Johannesburg (UJ).