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Sponsored by the Department of Science and Technology Volume Volume 26 Number 3 • August 2015 Sponsored by the Department of Science and Technology Volume 26 Number 3 • August 2015 CONTENTS 2 Reliability benefit of smart grid technologies: A case for South Africa Angela Masembe 10 Low-income resident’s preferences for the location of wind turbine farms in the Eastern Cape Province, South Africa Jessica Hosking, Mario du Preez and Gary Sharp 19 Identification and characterisation of performance limiting defects and cell mismatch in photovoltaic modules Jacqui L Crozier, Ernest E van Dyk and Frederick J Vorster 27 A perspective on South African coal fired power station emissions Ilze Pretorius, Stuart Piketh, Roelof Burger and Hein Neomagus 41 Modelling energy supply options for electricity generations in Tanzania Baraka Kichonge, Geoffrey R John and Iddi S N Mkilaha 58 Options for the supply of electricity to rural homes in South Africa Noor Jamal 66 Determinants of energy poverty in South Africa Zaakirah Ismail and Patrick Khembo 79 An overview of refrigeration and its impact on the development in the Democratic Republic of Congo Jean Fulbert Ituna-Yudonago, J M Belman-Flores and V Pérez-García 90 Comparative bioelectricity generation from waste citrus fruit using a galvanic cell, fuel cell and microbial fuel cell Abdul Majeed Khan and Muhammad Obaid 100 The effect of an angle on the impact and flow quantity on output power of an impulse water wheel model Ram K Tyagi CONFERENCE PAPERS 105 Harnessing Nigeria’s abundant solar energy potential using the DESERTEC model Udochukwu B Akuru, Ogbonnaya I Okoro and Chibuike F Maduko 111 Efficiency analysis of an induction motor with direct torque and flux control at a hot rolling mill Rupert Gouws 116 Contemporary wind generators Udochukwu Bola Akuru and Maarten J Kamper 125 Impact of renewable energy deployment on climate change in Nigeria Udochukwu B Akuru, Ogbonnaya I Okoro and Edward Chikuni 135 Details of authors 140 Site usage reports for the JESA Reliability benefit of smart grid technologies: A case for South Africa Angela Masembe Energy Research Centre, University of Cape Town Abstract 1. Introduction The South African power industry faces many chal- The distribution sub-system in South Africa, much lenges, from poor performing networks, a shortage like many other countries in the world, is still based of generation capacity to significant infrastructure on 20th century technology (DBSA, 2012). backlog and an ageing work force. According to the According to NELT (2007) and SANEDI (2012a), Development Bank of South Africa (DBSA), the key 20th century technology cannot efficiently sustain a challenge facing the industry is ageing infrastruc- 21st century economy, and power networks need to ture. Smart grid technologies are a class of tech- be ‘modernized’. A report released in 2007 by the nologies that are being developed and used by util- National Energy Regulator of South Africa ities to deliver electrical systems into the 21st centu- (NERSA) on the state of the Electricity Distribution ry using computer-based remote control and Industry (EDI) infrastructure, indicated that automation. The main motive towards smart grid although there were pockets of good performance, technologies is to improve reliability, flexibility, assets needed urgent rehabilitation and investment accessibility and profitability; as well as to support (NERSA, 2007). A study conducted in 2008 by EDI trends towards a more sustainable energy supply. Holdings on the state of the distribution grid of the This study identifies a number of smart grid tech- country, revealed that the distribution grid infra- nologies and examines the impact they may have structure was ageing and poorly maintained, and on the distribution reliability of a test system. The that its state was steadily deteriorating. The study components on the selected test system are the estimated that the maintenance, refurbishment and same as those found on a South African feeder. The strengthening backlog in the distribution grid bulk of the load in test system was modelled using amounted to about 27.4 billion 2008 South African load data collected in South Africa. This study will Rand (2008 ZAR). This backlog was growing at an consider a number of different cases, with the base alarming rate of 2.5 billion ZAR per annum (EDI case incorporating the impact of aged infrastructure Holdings, 2008). The same study pointed out that on the reliability of the system. The smart grid tech- the current practices in the EDI do not promote nologies were then introduced into the system and business sustainability and economic growth. It also their impact on distribution reliability was deter- highlighted the fact that the increased use of an mined. These different cases were also compared to under-maintained distribution grid could be a the alternative of replacing the aged and worn out potential risk to the industry. infrastructure with new infrastructure. The findings A more recent report released in 2012 by the of this study indicate that the identified smart grid Development Bank of South Africa (DBSA) on the technologies improve the reliability of the system, State of South Africa’s Economic Infrastructure, mainly by decreasing the outage duration experi- identified ageing infrastructure as the key challenge enced by customers on the network. An even better for the electricity generation, transmission and dis- performance was achieved when the ageing infra- tribution sectors. The other challenges faced in the structure was replaced with new infrastructure. South African power industry include: poor per- formance networks, shortage of generation capaci- Keywords: distribution reliability, smart grid, feeder ty, significant infrastructure backlog, ageing work automation force, inability to effectively introduce renewable energy options into the grid, and the inability to effectively introduce demand response strategies (SANEDI, 2012b). The term ‘smart grid’ refers to a class of tech- 2 Journal of Energy in Southern Africa • Vol 26 No 3 • August 2015 nologies that are being developed and used by util- In this study, a momentary interruption is ities to deliver electrical systems into the 21st centu- defined as an interruption with duration greater ry using computer-based remote control and than 3 seconds but not longer than 5 minutes, as automation (NELT, 2007). Smart grid technologies defined by the NRS 048-6:2006 specification for have been proposed as one of the possible means the Electricity Supply Industry for medium voltage of implementing new technologies and techniques (MV) and low voltage (LV) systems (Chatterton et into the grids of different countries (SANEDI, al., 2006). 2012a). The main motive towards smart grid tech- nologies is to improve reliability, flexibility, accessi- 3. Smart grid technologies bility and profitability; as well as to support trends Smart grid technologies refer to a group of towards a more sustainable energy supply improved technologies and concepts, that use digi- (Slootweg, 2009). tal and other advanced technologies, to monitor This paper will focus on the improvement smart and manage the transmission of electricity from all grid technologies could have towards improving generation sources, to meet the varying electricity distribution reliability. demands of end users (IEA, 2011). In a broad sense, a “smart grid” refers to a conventional elec- 2. Distribution reliability tric power system equipped with these technologies Reliability may be defined as the probability of a for the purpose of reliability improvement, ease of system performing its required tasks, adequately for control and management, integrating of distributed a period of time and under set operating conditions energy resources and electricity market operations. (Billinton & Allan, 1992). This definition in itself One of the most appealing advantages of smart highlights the uncertainty surrounding the ability of grid technologies is the reduced reaction and the power system to perform as desired, and there- restoration time. This is most apparent when a fault fore, the purpose of power system reliability evalu- has occurred. Ordinarily when a disturbance causes ation and assessment, is to try and quantify the reli- a fault on the network, grid operators are unable to ability of a system for planning and decision mak- identify the exact location of the faulted section of ing. the feeder. The repair crew are dispatched, and Reliability indices are used extensively in the have to perform trial and error switching actions on power system industry as a means to quantify and circuit breakers and isolators, in an effort to find the assess reliability. Reliability indices measure the fre- exact location of the fault. This can take a consider- quency, duration and severity of disturbances on able amount of time during the day and more espe- the network and give insight into the performance cially at night or during unfavourable weather con- of the system. These indices can be regarded as ditions, resulting in an increased outage duration being predictive indices or past performance (Kazemi, 2011). There are a number of smart grid indices. The indices considered in this study are technologies which have been developed in order System Average Interruption Duration Index to reduce the fault location time. These are dis- (SAIDI), System Average Interruption Frequency cussed below: Index (SAIFI), Momentary Average Interruption Frequency Index (MAIFI) and Expected Energy Not i) Distance to fault estimator Supplied (EENS). These indices are calculated as Fault locators reduce the impact of faults as they follows (Billinton & Allan, 1994; Brown, 2006): speed up the restoration process, by allowing isolat- ing and switching operations to be performed much faster (Morales-España et al., 2009). Distance to sum of customer interruption duration SAIDI = (1) fault estimators, are an optional module of modern total number of customers distribution protection equipment which can be used for estimating the fault location. When a fault total no customer momentary interruptions MAIFI = (2) occurs, this module calculates the fault location as a total number of customers served distance from the substation to the fault.
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