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Dr. Ashraf Zaghwan
PhD holder

Basic Info


Research Keywords & Expertise

0 Complex Systems
0 Emergence
0 Energy Losses
0 Tiny Initiated Events
0 Demand side management (DSM)

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Short Biography

Dr. Ashraf Zaghwan his PhD focused on Complex Systems Science from the Entrepreneurship, Commercialisation and Innovation Centre, Faculty of the Professions, the University of Adelaide, Australia. His PhD research investigates the roots of energy loss in electricity smart grid systems augmented by renewable resources. He received his MSc in Applied Project Management from the University of Adelaide. He has taught at the University of Adelaide and Torrens University. Prior to studying at the University of Adelaide, he worked as Marine Superintendent, Marine Chief Engineer, Second Engineer and Third Engineer. He received his BSc in Marine Engineering (Power Systems) and his Marine Chief Engineer Competence Certificate from the Academy of Maritime Studies, Libya. Ashraf is approaching his research through the lenses of leading ideas in the emerging field of Complexity Thinking to explore concepts and tools that apply to case studies in complex manipulation communities.

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Journal article
Published: 28 June 2021 in Sustainability
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This research draws attention to the potential and contextual influences on energy loss in Australia’s electricity market and smart grid systems. It further examines barriers in the transition toward optimising the benefit opportunities between electricity demand and electricity supply. The main contribution of this study highlights the impact of individual end-users by controlling and automating individual home electricity profiles within the objective function set (AV) of optimum demand ranges. Three stages of analysis were accomplished to achieve this goal. Firstly, we focused on feasibility analysis using ‘weight of evidence’ (WOE) and ‘information value’ (IV) techniques to check sample data segmentation and possible variable reduction. Stage two of sensitivity analysis (SA) used a generalised reduced gradient algorithm (GRG) to detect and compare a nonlinear optimisation issue caused by end-user demand. Stage three of analysis used two methods adopted from the machine learning toolbox, piecewise linear distribution (PLD) and the empirical cumulative distribution function (ECDF), to test the normality of time series data and measure the discrepancy between them. It used PLD and ECDF to derive a nonparametric representation of the overall cumulative distribution function (CDF). These analytical methods were all found to be relevant and provided a clue to the sustainability approach. This study provides insights into the design of sustainable homes, which must go beyond the concept of increasing the capacity of renewable energy. In addition to this, this study examines the interplay between the variance estimation of the problematic levels and the perception of energy loss to introduce a novel realistic model of cost–benefit incentives. This optimisation goal contrasted with uncertainties that remain as to what constitutes the demand impact and individual house effects in diverse clustering patterns in a specific grid system. While ongoing effort is still needed to look for strategic solutions for this class of complex problems, this research shows significant contextual opportunities to manage the complexity of the problem according to the nature of the case, representing dense and significant changes in the situational complexity.

ACS Style

Ashraf Zaghwan; Indra Gunawan. Energy Loss Impact in Electrical Smart Grid Systems in Australia. Sustainability 2021, 13, 7221 .

AMA Style

Ashraf Zaghwan, Indra Gunawan. Energy Loss Impact in Electrical Smart Grid Systems in Australia. Sustainability. 2021; 13 (13):7221.

Chicago/Turabian Style

Ashraf Zaghwan; Indra Gunawan. 2021. "Energy Loss Impact in Electrical Smart Grid Systems in Australia." Sustainability 13, no. 13: 7221.

Journal article
Published: 22 March 2021 in Designs
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This study utilises the Pareto approach to highlight the energy losses that mainly originate from the phenomena of tiny, initiated events created by end-users of electricity in Australia. Simulation modelling was applied through two stages to examine residential households’ electricity consumption behaviour in New South Wales, Australia. Stage one analysis applied Hierarchical agglomerative clustering and a dendrogram to denote the respective Euclidean distance between the different clusters. Heat maps and threshold value area charts were used to compare the mean power demand for six respective clusters. Stage two used ‘sensitivity analysis’ to investigate how uncertainty in the electricity demand can be allocated to the uncertainty of energy losses. The findings envision practical solutions to dealing with the variability of energy losses and the proposal to set new demand-side strategies associated with individuals. Retail prices of electricity in Australia have risen by roughly 60% since 2007. The research contributes to knowledge about the roots of energy losses in Australia, creating a $210M cost value. Energy losses are of significant economic value, while also impacting energy security. The first limitation of this study is using approaches from complexity theory to grasp the philosophical issues behind the research design and clarifying which insights suit what kind of evidence, thus identifying the data that needed to be collected. The second limitation is that this study’s methodology used a mostly quantitative approach that describes and explains a complex phenomenon in depth more than exploring and confirming that phenomenon. The third and final limitation is that this study’s context is also limited regarding selected sample criteria. The context is limited to a particular demographic area in New South Wales (NSW) in Australia and is also limited to residential houses (not industrial or commercial), which was opposed by data availability and access. The research draws on ‘peak and off-peak’ scales of electricity demand cause energy losses. The research shows the role of the phenomena of spontaneous emergence as a non-linked constraint which is the main issue that splits the optimal solution into pieces and significantly complicates the solution task. Demand side management (DSM) of electricity can be improved from this to construct new demand-side strategies. The study is structured around understanding the consequences of the scalability of events and the clustering dynamic of non-linearity through relevance complexity concepts exclusive to spontaneous emergence (SE), power laws (PLs), Paretian approach (PA), and tiny initiated events (TIEs). We examined the issues of the spontaneous emergence of non-linear, dynamic behaviour involved in the electricity demand of end-users on the basis of pushing individual systems of end-users to the edge of self-organised criticality (SOC). Revising the demand system’s complexity has value in constituting a core domain of interest in what is new in the field of demand side management (DSM), thus contributing to understanding end-users’ behaviour-driven energy losses from both theoretical and empirical perspectives.

ACS Style

Ashraf Zaghwan; Indra Gunawan. Resolving Energy Losses Caused by End-Users in Electrical Grid Systems. Designs 2021, 5, 23 .

AMA Style

Ashraf Zaghwan, Indra Gunawan. Resolving Energy Losses Caused by End-Users in Electrical Grid Systems. Designs. 2021; 5 (1):23.

Chicago/Turabian Style

Ashraf Zaghwan; Indra Gunawan. 2021. "Resolving Energy Losses Caused by End-Users in Electrical Grid Systems." Designs 5, no. 1: 23.

Journal article
Published: 06 February 2021 in Solar Energy and Sustainable Development journal
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To assess the wind energy potential at any site, the wind power density should be estimated; it evaluates the wind resource and indicates the amount of available wind energy. The purpose of this study is to estimate the monthly and annual wind power density based on the Weibull distribution using wind speed data collected in Zwara, Libya during 2007. The wind date are measured at the three hub heights of 10m, 30m, and 50m above ground level, and recorded every 10 minutes. The analysis showed that the annual average wind speed are 4.51, 5.86, 6.26 m/s for the respective mentioned heights. The average annual wind power densities at the mentioned heights were 113.71, 204.19, 243.48 , respectively.

ACS Style

Alhassan A. Teyabeen; Fathi R. Akkari; Ali E. Jwaid; Ashraf Zaghwan; Rehab Abodelah. Assessment of Wind Energy Potential In Zwara, Libya. Solar Energy and Sustainable Development journal 2021, 8, 1 .

AMA Style

Alhassan A. Teyabeen, Fathi R. Akkari, Ali E. Jwaid, Ashraf Zaghwan, Rehab Abodelah. Assessment of Wind Energy Potential In Zwara, Libya. Solar Energy and Sustainable Development journal. 2021; 8 (2):1.

Chicago/Turabian Style

Alhassan A. Teyabeen; Fathi R. Akkari; Ali E. Jwaid; Ashraf Zaghwan; Rehab Abodelah. 2021. "Assessment of Wind Energy Potential In Zwara, Libya." Solar Energy and Sustainable Development journal 8, no. 2: 1.

Journal article
Published: 01 May 2018 in Ocean Engineering
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ACS Style

Nagi Abdussamie; Ashraf Zaghwan; Mohamed Daboos; Ismail Elferjani; Ahmed Mehanna; Wenming Su. Operational risk assessment of offshore transport barges. Ocean Engineering 2018, 156, 333 -346.

AMA Style

Nagi Abdussamie, Ashraf Zaghwan, Mohamed Daboos, Ismail Elferjani, Ahmed Mehanna, Wenming Su. Operational risk assessment of offshore transport barges. Ocean Engineering. 2018; 156 ():333-346.

Chicago/Turabian Style

Nagi Abdussamie; Ashraf Zaghwan; Mohamed Daboos; Ismail Elferjani; Ahmed Mehanna; Wenming Su. 2018. "Operational risk assessment of offshore transport barges." Ocean Engineering 156, no. : 333-346.