Department of Electrical and Electronic Engineering
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Electrical and Electronic Engineering is an exciting and dynamic field. Electrical engineers are responsible for the generation, transfer and conversion of electrical power, while electronic engineers are concerned with the transfer of information using radio waves, the design of electronic circuits, the design of computer systems and the development of control systems such as aircraft autopilots. These sought-after engineers can look forward to a rewarding and respected career.
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Browsing Department of Electrical and Electronic Engineering by browse.metadata.advisor "Bekker, Bernard"
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- ItemCharacterizing variable renewable energy generation uncertainty towards improved forecasting and operational decision making(Stellenbosch : Stellenbosch University, 2022-12) Mararakanye, Ndamulelo; Bekker, Bernard; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: As its first novel contribution, this dissertation investigates the broad challenge of understanding the impacts of integrating a high share of variable renewable energy (VRE) generators into power systems. These generators are variable, uncertain, non-synchronous and location constrained, introducing a wide range of impacts that are unique to a specific region and require different input data, models, and simulation tools to study. It is resource intensive to study all these impacts and therefore important to effectively identify and study only those impacts that are most relevant to the region under consideration. In addressing this challenge, this dissertation firstly identifies three main factors that influence the VRE integration impacts in different regions: available resources, penetration level, and grid characteristics. Thereafter, the international experience is used to understand how these three factors contributed to the issues that were experienced in different regions. The outcome of this investigation is a framework for comprehending VRE integration issues based on available renewable resources, penetration level, and grid characteristics of a region under consideration. This framework can be used by network planners, policymakers, and grid operators to prioritize VRE integration issues of concern to their region prior to conducting detailed studies, thereby reducing the resources required. This dissertation identifies wind power forecasting as key in mitigating some of the impacts introduced by high share of VREs. Within the context of wind power forecasting, this dissertation investigates the challenge of aggregating decentralized forecasts. These forecasts are typically optimized for local conditions because the individual wind farms do not have access to power data from other wind farms. Simply adding these decentralized forecasts together at the point where these forecasts are received (typically the system operator) may not capture some of the common spatial and temporal correlations of wind power, thereby lowering the potential accuracy of the aggregated wind power forecast. In response to this challenge, this dissertation proposes explanatory variables that are used to train the machine learning models to derive aggregated point and probabilistic wind power forecasts from decentralized forecasts. The proposed explanatory variables include clusters of point forecasts (to account for spatial correlations between wind farms), hour of day (to account for diurnal cycles), month of year (to account for seasonal cycles) and, atmospheric states (to account for correlations due to large-scale atmospheric circulations). Training machine learning models using these explanatory variables results in a significant improvement in the accuracy of aggregated forecasts, becoming the second novel contribution of this dissertation. This is particularly important in regions where individual wind farms generate their own forecasts. This dissertation also acknowledges the fact that wind power forecasts are not always perfect, giving rise to the need to understand and estimate wind power forecasting uncertainty. One of the challenges concerning the characterization of forecasting uncertainty is that some of the parametric distributions (normal, beta, Weibull, etc.) commonly used for modeling forecast errors may be inappropriate in representing extreme errors. While non-parametric approaches can be accurate, extreme errors often do not occur frequently enough to make accurate non-parametric inferences. There remains a need to find a parametric model that best represents the extreme errors. To address these challenges, this dissertation identifies a suitable parametric distribution for representing extreme errors, investigates some of the factors that may influence extreme errors, and proposes a suitable model for representing spatial correlations of extreme errors between wind farms. Therefore, the third novel contribution of this dissertation is to propose modeling approaches for improving the estimation and understanding of extreme errors. This is an important step toward better allocation of operating reserves to account for forecasting uncertainty. Continuing within the context of forecasting uncertainty, it is known that the conditional forecast error distributions change with the wind power forecast mostly due to the slope of wind to power conversion curves. The variance is often small at low and high power forecasts but large at mid-range power forecasts. The forecast error distribution is skewed right at low power forecasts, symmetric at mid-range power forecasts, and skewed left at high power forecasts. As a result, some of the commonly used distributions for modeling forecast errors may lack the flexibility required to represent conditional forecast error distributions at different wind power forecasts. As a fourth novel contribution, this dissertation proposed and evaluated an approach for deriving the conditional forecast error distribution for a given wind power forecast. These conditional distributions typically contain more probabilistic information (as compared to unconditional distribution), which can be used to improve reserve allocation in grids with high share of wind generators.
- ItemA comprehensive methodology for impact assessment studies of energy storage systems on low voltage distribution feeders(Stellenbosch : Stellenbosch University, 2020-12) Rhoda, Courtney; Bekker, Bernard; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: This research investigates the technical impacts of energy storage systems (ESSs) on low voltage (LV) residential feeders. A critical literature review on the existing impact assessment methodologies informs on the requirements of an efficient methodology that ensures the accurate and detailed assessment of feeder performance under ESS penetration. Based on the review’s findings, a comprehensive stochastic-probabilistic methodology is proposed that directly accounts for the unpredictability in customer behaviour and the subsequent impact on the diversity and variability in simulation inputs and outcomes of load flow analysis (something that most impact assessment methodologies do not adequately account for). The proposed methodology makes use of the Monte Carlo Simulation method as a stochastic simulator to simulate the uncertainty in the feeder placement of ESSs, and the Herman-Beta extended algorithm to solve the probabilistic load flow analysis. This proposed methodology can be used to assess the hosting capacity of radial LV distribution feeders to increasing penetrations of ESSs. The simulation results, from detailed and comprehensive input modelling, can provide helpful and more accurate and representative information to distribution network planners and policymakers, than simplified methodologies.
- ItemThe Coordination and control of smart inverters utilizing Volt-VAr and Volt-Watt in low voltage networks, and opportunities for South Africa(Stellenbosch : Stellenbosch University, 2022-04) Xavier, Ria; Bekker, Bernard; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: Increasing photovoltaic (PV) penetration in the low-voltage (LV) distribution network leads to grid-interconnection issues for electric utilities. These issues include voltage violations, equipment overloading and frequency instability. To mitigate these problems, advanced smart inverter functionality is becoming increasingly popular in states and countries with high renewable energy penetration levels. Although smart inverters have a wide range of benefits for the utility, these benefits are limited to the local level due to autonomous inverter control. This research investigates the benefits of coordinated inverter control in mitigating voltage violations in LV feeders due to increasing PV penetrations. A critical literature review on the grid interconnection requirements and smart inverter functionality guidelines informs on the gaps that need to be addressed to allow for increased smart inverter deployment in South Africa. The literature review also explores the benefits of distributed energy resource management systems (DERMS) and virtual power plants (VPPs), and the requirements for each platform. Based on the literature review’s findings, a simulation has been conducted to investigate the benefits of coordinated smart inverter voltage regulation control, particularly Volt-VAr and Volt-Watt, to increase hosting capacity in LV networks. The proposed methodology considers the feeder-wide voltage conditions instead of local point of connection (PoC) conditions using sensor measurements, and the fairness of voltage regulation and active power curtailment among customers on a feeder. This proposed methodology can be used as an intermediate solution for coordinating smart inverters without the use of extensive communication infrastructure and advanced aggregating platforms. The simulation results show an improvement in voltage profiles using coordinated Volt-VAr and Volt Watt inverter control and feeder-wide awareness. The improved voltage profiles can accommodate higher levels of PV penetration and thus increase hosting capacities in LV feeders.
- ItemIncorporating short-term operational constraints into long-term generation planning: a Namibian case study(Stellenbosch : Stellenbosch University, 2023-03) Mouton, Daniello; Bekker, Bernard; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: Namibia possesses an abundance of natural energy resources that have the potential to be used for electricity production. At present, Namibia does not have enough generation capacity to meet its electricity demand. The Namibian Ministry of Mines and Energy thus published the National Integrated Resource Plan in 2016 with a view to meet 100% of its peak load demand and at least 75% of its energy demand through internal or local sources. This study is motivated by three key issues identified in the Namibian generation expansion plan contained in the National Integrated Resource Plan, namely: (1) challenges presented by an increased amount of variable renewable energy present in Namibia’s power system; (2) the sole use of traditional reliability metrics such as Loss of Load Probability and Expected Unsupplied Energy; and (3) the absence of any flexibility assessment of the proposed power system. This study hypothesises that the sole use of conventional reliability metrics in long-term generation expansion planning does not guarantee adequate flexibility in power systems with high shares of variable renewable energy. The flexibility constraints of the power system are also considered with a view to test the hypothesis, which is necessary in the context of the three aforesaid key issues. To this end, this study makes use of emerging generation planning techniques, including Flexibility Assessment Methods, that are capable of ensuring better evaluation of operational reliability. By simulating the Namibian power system within the context of the recently published National Integrated Resource Plan, the hypothesis is proven, and this study concludes that the sole use of conventional reliability metrics in Namibia’s long-term generation expansion plan does not guarantee adequate flexibility in its power systems that has a high share of variable renewable energy.
- ItemA probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network(Stellenbosch : Stellenbosch University, 2020-03) Waswa, Lewis; Bekker, Bernard; Chihota, Justice; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: Increased solar photovoltaic (PV) installation on to the grid has led to increased technical challenges in electricity network operations. These challenges mainly stem from the design structure of the grid, which only allows unidirectional power flow. This results in several challenges including violation of voltage limits, tripping of network protection systems and distribution line overloads among other issues. These challenges are mainly restricted to the distribution networks, as most solar PV small-scale embedded generators (PV SSEGs) are connected to the distribution networks, whose conditions are, in most cases, not remotely monitored. This results in increased challenges experienced by the networks in terms of network planning, distribution network operations, maintenance, regulation and grid control. To manage these challenges, the distribution operator needs to estimate the total capacity of solar PV installed on the distribution network, in addition to how much of that capacity is embedded in the network’s net demand, which is important in determining the condition of the network at any particular time. Several methods have been used to estimate the capacity of solar PV SSEGs installed in an area. Most studies apply remote sensing and computer vision algorithms to count the number of solar PV panels found in an area. Analysis of these studies indicate that the results obtained cannot be used in determining the condition of the network as they only determine the capacity of solar PV in an area. Secondly, disaggregation studies have largely been used to quantify the installed solar PV capacity embedded in the net demand of a feeder or network. These methods assume a multi-variable approach which requires multiple inputs that are not readily available. This study introduces a novel probabilistic method that applies Monte Carlo methods to quantify the solar PV SSEGs embedded in the net demand of a low voltage feeder. Historical demand, net demand and the solar PV output is used to determine the solar PV capacity embedded in the net demand of a feeder. The accuracy of the method is tested using simulated net demand and actual measured net demand metered from households connected on carefully selected feeders. Results demonstrate that the method performs well where the historical demand and the net metered demand are obtained from similar customer classes. Therefore, it is concluded that it is possible to estimate the capacity of solar PV SSEGs embedded in the net demand obtained from a feeder by analysing and comparing the net demand of that feeder and the historical demand of a similar customer class feeder.
- ItemValuation of pumped storage in capacity expansion planning – a South African case study(Stellenbosch : Stellenbosch University, 2022-04) Van Dongen, Caroline; Bekker, Bernard; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: On the South African grid, pump storage schemes offer a range of benefits which can assist in the integration of increased variable renewable energy generation. The Integrated Resource Plan for South Africa currently proposes adding gas turbines and batteries to the future grid for peaking capacity and increased flexibility, with no added pump storage schemes. This thesis investigates the value of the services and contributions pumped storage provides the grid, the capital costs and history of this technology in order to determine its potential future role. The research aims to address the possible misconception of limited pump storage scheme site availability by providing an overview of site feasibility studies, including estimated cost projections and utilises these values in an energy optimisation model to investigate the economic case for pump storage.