Reducing energy costs within schools in South Africa using solar and intelligent hot water interventions
Thesis (MScEng)--Stellenbosch University, 2019.
ENGLISH ABSTRACT: The educational gap within socio-economic groups in South Africa is immense and learners within no-fee schools are at a disadvantage with most of the available funding being used for personnel salaries. As the energy costs in South Africa rise, the remaining non-personnel funding will decrease, limiting spending on teacher-support materials and school maintenance. This has led to lasting problems within these communities and constrains the ability of the education system to provide learners of these schools with a pathway out of poverty. Schools are currently billed on commercial and industrial tariffic structures, and by reducing their energy usage and maximum monthly demand, money can be saved to be better spent improving the quality of education delivered. To address this problem a comprehensive system capable of estimating the potential financial viability of solar and load-shifting interventions within a school environment was developed. A method capable of determining the energy usage within schools was developed. This process creates a generic energy consumption profile for a building from measured energy usage data of a subset of schools, and is expanded to scale the load profile using only usage data from utility bills as well as seasonal dates to produce a load forecast. The method was validated using five datasets each containing the hourly energy usage measurement data from schools over a period of three years, and was capable of forecasting the yearly energy consumption of the schools to within an averaged accuracy of 5% while estimating the maximum monthly demand to 6% of the measured usage. A PV optimisation technique was implemented using the forecast to estimate the potential profitability of various solar system sizes by determining the internal rate of return and the utilisation of the system's generating capacity. It was able to identify the optimal system size of the schools with the best return on investment, presenting itself as a valuable tool for reducing the financial burden many schools face. Three control schemes of intelligent water heater scheduling were researched. Firstly, a priority-based scheduler was configured to heat water using the school's water usage history while diverting any excess solar energy to the water heaters to exploit their energy storage capabilities, increasing the school's energy bill savings to 23.2% per month. Secondly, a bi-thermal control method was added to the priority-based scheduler, employing a temperature delta to increase the amount of solar energy to be stored within the water heater tank while minimising their grid reliance and improving the monthly savings to 24.8% per month. Finally, a demand-limiter control scheme was implemented in conjunction with bi-thermal control resulting in large demand-charge savings and an average energy bill reduction of 26.7% per month, producing the maximum savings while maintaining suitable levels of user comfort.
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