Masters Degrees (Electrical and Electronic Engineering)

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Browsing Masters Degrees (Electrical and Electronic Engineering) by Author "Avenant, Jason"

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    Residential load modelling to predict household consumption for design of photovoltaic systems
    (Stellenbosch : Stellenbosch University, 2022-12) Avenant, Jason; Booysen, Thinus; Rix, Arnold J. ; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: South Africa’s electrical network is restricted, and its supply is variable. Adoption of solar systems is a potential solution. Despite the abundance of sunshine in South Africa, the adoption of domestic rooftop solar has remained limited. The unequal distribution of wealth and linked usage, a legacy of apartheid, and the uncertain cost/benefit of large-scale solar installations play a significant role in this. To mitigate the uncertainty of large-scale solar deployment, PV system design may be predicted using simulation. This provides users with a better understanding of the energy savings and partial independence that PV system adoption can offer from the volatile grid . Domestic electricity usage and solar insolation are key components in the design of a PV system. Because usage patterns vary and solar insolation fluctuates, these variations must be considered when properly analysing the viability of PV adoption. In South Africa, a lack of load data hinders large-scale solar PV system planning and implementation. Although high-resolution data on individual residential energy use is scarce in South Africa, the DELS (Domestic Electrical Load Study) dataset, which contains data for 14 945 households sampled hourly, is an exception. Given the dataset’s geographical and demographic representation, it provides an intriguing approach of assessing large-scale solar PV implementation. To use the DELS dataset in this way, a model needs to be developed to statistically describe the measured usage profiles for each household. Thereafter, the model can be used to simulate household usage, the resultant profiles of which can be compared to simulated solar PV profiles and used to size solar installations for each household. In this thesis, we presented a load model and subsequent load synthesiser for South African residential households. Because data quality and availability are difficult to obtain in developing countries such as South Africa, we designed the model to function with minimal training data. In addition to the synthesiser, we conducted a case study to evaluate the synthesiser’s suitability for the sizing and design of fixed-axis rooftop PV systems based on the synthetic profiles. A data reduction framework to extract key features from representative daily load profiles was employed. The developed models accounted for the effects of seasonal and day-of-week trends on household consumption patterns. A sum-of-Gaussian (SOG) model was used to describe load profile shapes. The model made use of the assumption that residential households’ load behaviour would have two distinct peaks, one in the morning and one in the afternoon, representing their work/school schedule. A probabilistic model is used to simulate the distribution of peak amplitudes in households. Employing hypothesis tests we fitted probability distributions to the measured peaks amplitude values. A load synthesiser was created by combining the two models. The synthesiser simulates different seasons and days (weekdays, Saturdays and Sundays). Depending on the season and day type being synthesised, we generate load profiles using a SOG-based model that was trained on a relating seasonal and day type subset. To scale the amplitude of the synthetic load profile, we created synthetic amplitude values by simulating the amplitude distribution using the probabilistic model. In this way, the synthesiser can simulate load profiles with similar statistical characteristics and shapes to the measured daily load profile. The proposed synthesiser was used to assess its suitability for the design of fixed-axis rooftop PV systems. We designed a generic PV system that was used to produce PV simulations using SAM (System Advisory Model). The synthesised days were used to scale the system’s size to conform to a design requirement that the PV system export no more than 15% of the energy it produces. The developed probabilistic model was assessed by comparing the measured peak amplitudes, and shown to accurately portray the measured values’ statistical properties. The synthetic and measured distributions were virtually identical, with the first quantile of values showing the biggest difference, which showed that the poor performance was limited to very low consumption levels. Synthetic days generated for one calendar year of days were used to evaluate the sum-of-Gaussian based model. Utilising the SMAPE (symmetric mean absolute percentage error) and MAE (mean absolute error), we compared the synthetic profiles shape to the measured profiles. We discovered that even though the model performed well in terms of reproducing the shape of the RDLPs, the level of accuracy varied when simulating a year’s worth of dates. This was most likely brought on by the assumptions and approximations made regarding the shape of residential load profiles. To evaluate the performance of the synthesiser, 900 households that had more than a year’s worth of measured days were compared with a year’s worth of synthetic data. The synthesiser was tested to determine whether it captured the shape of the daily load profiles throughout the year. The tests determined if it captured the statistical characteristics of an individual household’s load as well as all households and if it captured the statistical characteristics of the aggregated normalised grid of 900 households. We discovered that the synthesiser tended to overestimate peak loads while underestimating off-peak loads. Rooftop solar PV systems were sized using measured data and compared with ones sized using synthesised data. Those sized using synthetic data proved to be oversized by a small amount – less than a PV module per household. The model behaved as expected, the synthesiser created slightly inflated peak loads, but proved to be a useful tool for assessing fixed axis rooftop PV systems. As a result, the project goals were achieved and objectives fulfilled. Additional adjustments to the synthesiser and models for future work were suggested. The project code is available online1.