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Theses and dissertations (Engineering and Built Environment)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/10

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    Kernel estimation modelling and optimization of hybrid power system for a typical South African rural area
    (2023-09) Magenuka, Thand’uxolo Kenneth; Kabeya, Musasa; Akindeji, Kayode Timothy
    To increase the accessibility of electricity even to those rural sparsely scattered isolated rural regions, renewable energy seems to be a viable and sustainable option. Before investing in renewables in these areas, a feasibility study is of paramount importance starting with assessing and determining the amount of available solar irradiance and wind speeds for the area. In addition, a techno-economic feasibility study is of paramount importance to determine the most economical and sustainable standalone hybrid system. This research presents a study using a nonparametric kernel density estimation method to determine solar irradiance and wind speeds. In addition to this kernel determination method, the study performs a feasibility analysis using a hybrid renewable energy system that consists of two renewables with biodiesel and battery backup to supply the energy demands of a rural household in South Africa. The research commences with a literature review of several probability distribution functions (pdfs) commonly used in testing both solar irradiance and wind speeds. It established that not all sites can be defined by the same pdf and there is no science in selecting a distribution function but rather random testing of a range of functions. The parametric probability functions tested in this work are Gamma, Weibull, and Lognormal. The work then compares the performance of these parametric pdfs with the nonparametric kernel density estimation method which this study advocates for its application. In judging the performance and correctness of these pdfs, mean bias error (mbe) and root mean square error (rmse) are used as performance test criteria for the parametric probability distribution function. As for the nonparametric pdf which this research advocates for its use, an integral squared error, ISE is used for the presentation assessment with the conventional parametric normal distribution. From the results, it is observed with the proposed nonparametric kernel density estimator gives precise estimation and improved adaptableness, as opposed to the widely used conventional parametric distribution for both the use in solar irradiation and wind, speeds estimations. In addition, the research results demonstrated that the commonly used Epanechnikov and Gaussian KDE methods were the most adjustable methods for all seven tested stations. The second aspect of the study applies the tested data to design and perform a feasibility study of using a hybrid renewable energy system that consists of two renewables with biodiesel and battery backup to supply energy demands for a typical rural household. Thus, the study makes use of a simulation to design and determine an optimized hybrid renewable energy system for application in rural households. The energy resources considered for this standalone hybrid system are solar PV, wind, diesel generator, and a storage battery system. In performing the system simulation and optimization concerning economic viability, sustainability, energy efficiency, and environmental impact is carried out using the Hybrid Optimization Model for Electric Renewables (HOMER) simulation and optimization software tool. Concerning the results obtained, HOMER gave seven best-optimized systems. In breaking down the seven optimized results, four of the results were hybrid energy systems and three with only one energy resource. Moreover, from these results, three systems were pure green energy supplied and not utilizing any diesel generator (DG). The best-optimized system for this rural household consisted of PV/DG with an NPC of $ 72,720, while the system which utilized all resources available was second-ranked with an NPC of $ 79,272. The use of only renewable resources for this region was fourth-ranked with NPC of $ 86,760. The study demonstrates the feasibility and viability of having rural areas benefit from electricity access. Moreover, this study will contribute towards the strides of just energy transition envisaged by the country in solving the energy crisis currently being experienced.
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    Coordinated control of conventional power sources and plug-in hybrid electric vehicles for a hybrid power system
    (2022-05) Adbul-Kader, Mohammed Ozayr; Akindeji, Timothy Kayode; Sharma, Gulshan
    Globally, the requirement for renewable and clean energy technologies is becoming vastly popular. With the high implementation of solar and wind energy systems, together with plugin hybrid electric vehicle (PHEV) aggregators, energy costs can be minimised, greenhouse gas emissions decrease, and overall maintenance becomes reduced. The constant increase of load demand is becoming a challenge for the current power systems, with difficulties including stability concerns and excessive regulations by the government. Due to irradiance and wind speed fluctuations, the solar and wind energy system’s non-linearity affects the existing power system stability. The growth of the electric vehicle industry has also shed new light on potential auxiliary services that can be provided, as and when required, to the power system. Hence, this research examines the potential control strategies that are required to maintain the system in steady-state conditions after disturbances that occur with higher penetration of renewable energy systems (RESs) and PHEVs. The case study models a isolated two-area thermal type power system that is interconnected through an AC tie-line. Three scenarios are modelled, simulated and analysed. The first scenario models a isolated thermal power system with PHEVs with two areas which utilises a fractional order proportional integral derivative (FOPID) controller in each area. The resulting model is analysed to see the effects of PHEVs coupled with FOPID on the power system. The second scenario models a isolated two-area thermal power system with RES and utilises a fuzzy type-2 (FT2) FOPID controller in each area. The RES penetration istested for its non-linearity effect on the isolated power system, and the error is reduced by an advanced controller that uses artificial intelligence techniques. The third scenario is modelled as an isolated two-area thermal power system with PHEVs and RES coupled with neural network predictive controller (NNPC) in each area. The three scenarios are simulated in MATLAB/Simulink with results displayed graphically and numerically. The results show that the integration of PHEVs for load and/or storage in the multi-area power system, and the proposed control methods for each scenario, have the best dynamic response with the least error, no oscillations and the fastest response to steady state condition.