Resolving Energy Losses Caused by End-Users in Electrical Grid Systems

Abstract

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.

Energy Loss Impact in Electrical Smart Grid Systems in Australia

“…This problem of demand volatility has been further increased due to the growing penetration of renewable energy sources as it lacks a clear economic incentive for stakeholders that eventually translates to financial pressure. This cost burden issue sheds light on the necessity of maintaining demand stability and supply security as one of the significant challenges in the future [19,27].…”

“…This cyclic issue escalates to more complex levels when all physical limitations of the power generation are rated. Therefore, this stage of the study neglects this complexity and assumes that the scenario cost parameter relies on the variables' values of the real time demand set as a limit to the price-sensitive load depending on the cost incurred based on the load capacity factor [27,56]. Figure 17 shows us that the concern of SA analysis is centred on threats of energy loss to home sustainable systems and the need to assess evidence, which may be harmed by many individual home systems that no doubt exist.…”

“…This cyclic issue escalates to more complex levels when all physical limitations of the power generation are rated. Therefore, this stage of the study neglects this complexity and assumes that the scenario cost parameter relies on the variables' values of the real time demand set as a limit to the price-sensitive load depending on the cost incurred based on the load capacity factor [27,56].…”

“…Therefore, the purpose of the PLD and ECDF tests in testing the normality of time series data is to compare (the macro) PLD empirical distribution function (the mean of CDF for all data pins) against (the micro) ECDF empirical distribution function (the mean of CDF for individual pins in the sample data, i.e., Figures 18,19,[24][25][26][27]. This is to measure the discrepancy between PLD and ECDF.…”

Estimation of electrical energy consumed during the construction of residential houses in San Pedro Sula, Cortés, Honduras

“…Udawattha and R.U. Halwatura where they mention the costs of the construction of residential houses, where it is also mentioned that the electrical energy used for the construction of houses is not so high if we compare it to the other expenses that exist such as labor, materials and construction permits [9] [10] [11].…”