Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Application of a Bayesian Network complex system model to a successful community electricity demand reduction program

Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.

Improved residential electricity demand management through analysis of the customer perspective

Peak electricity demand requires substantial investment to update transmission, distribution and generation infrastructure. A successful community peak demand reduction project was examined to identify residential consumer motivational and contextual factors involved in their decision to adopt/not adopt interventions. Energy professionals actively worked to achieve community 'peer' membership and by becoming a trusted information source, facilitated voluntary home energy assessment requests from over 80% of the residential community. By combining and tailoring interventions to the specific needs and motivations of individual householders and the community, interventions promoting energy conservation and efficiency can be effective in achieving sustained reduction in peak demand.

Measurement of standby power for selected electrical appliances in Australia

High growth in the uptake of electrical appliances is accounting for a significant increase in electricity consumption globally. In some developed countries, standby energy alone may account for about 10% of residential electricity use. The standby power for many appliances used in Australia is still well above the national goal of 1 W or less. In this paper, field measurements taken of standby power and operating power for a range of electrical appliances are presented. It was found that the difference between minimum value and maximum value of standby power could be quite large, up to 22.13 W for home theatre systems, for example. With the exception of home audio systems, however, the annual operating energy used by most electrical appliances was generally greater than the annual standby energy. Consumer behaviour and product choice can have a significant impact on standby power and operating power, which influences both energy demand and greenhouse gas emissions.

Designing well : sustain-able interaction design and vegetarianism

Designing Well: Vegetarianism Sustainability and Interaction Design, focuses on the field of Interaction Design and is an exploration of how design can be reconsidered by employing a different critical lens – that of vegetarianism. By extending the eating analogy to design, other aspects of practice can be reframed and reviewed. This is done through a survey of different ways designers and artists have approached the problems of electricity use. This survey begins by looking at a number of functional products that are currently on the market, and then turns to consider a range of alternate approaches taken in research, art and critical design. The second half of the paper can be considered as a form of contextual review, as a survey of different approaches artists and designers employ to address a specific issue in and through practice. This ranges from pragmatic design to critical and radical interventions.

Materials and technologies for energy conversion, saving and storage: A global network enabling capacity-building for sustainable energy in developing countries

Access to energy is a fundamental component of poverty abatement. People who live in homes without electricity are often dependent on dirty, time-consuming and disproportionately expensive solid fuel sources for heating and cooking. [1] In developing countries, the Human Development Index (HDI), which comprises measures of standard of living, longevity and educational attainment, increases rapidly with per capita electricity use. [2] For these reasons the United Nations has been making a concerted effort to promote global access to energy, first by naming 2012 the Year of Sustainable Energy for All, [3] and now by declaring 2014-2024 the Decade of Sustainable Energy for All. [4]

Quantitative assessment of fault tolerant precision timing for electricity substations

Advanced substation applications, such as synchrophasors and IEC 61850-9-2 sampled value process buses, depend upon highly accurate synchronizing signals for correct operation. The IEEE 1588 Precision Timing Protocol (PTP) is the recommended means of providing precise timing for future substations. This paper presents a quantitative assessment of PTP reliability using Fault Tree Analysis. Two network topologies are proposed that use grandmaster clocks with dual network connections and take advantage of the Best Master Clock Algorithm (BMCA) from IEEE 1588. The cross-connected grandmaster topology doubles reliability, and the addition of a shared third grandmaster gives a nine-fold improvement over duplicated grandmasters. The performance of BMCA mediated handover of the grandmaster role during contingencies in the timing system was evaluated experimentally. The 1 µs performance requirement of sampled values and synchrophasors are met, even during network or GPS antenna outages. Slave clocks are shown to synchronize to the backup grandmaster in response to degraded performance or loss of the main grandmaster. Slave disturbances are less than 350 ns provided the grandmaster reference clocks are not offset from one another. A clear understanding of PTP reliability and the factors that affect availability will encourage the adoption of PTP for substation time synchronization.

Modeling electricity price events as point processes

Energy prices are highly volatile and often feature unexpected spikes. It is the aim of this paper to examine whether the occurrence of these extreme price events displays any regularities that can be captured using an econometric model. Here we treat these price events as point processes and apply Hawkes and Poisson autoregressive models to model the dynamics in the intensity of this process.We use load and meteorological information to model the time variation in the intensity of the process. The models are applied to data from the Australian wholesale electricity market, and a forecasting exercise illustrates both the usefulness of these models and their limitations when attempting to forecast the occurrence of extreme price events.

Semi-parametric forecasting of spikes in electricity prices

The occurrence of extreme movements in the spot price of electricity represents a significant source of risk to retailers. A range of approaches have been considered with respect to modelling electricity prices; these models, however, have relied on time-series approaches, which typically use restrictive decay schemes placing greater weight on more recent observations. This study develops an alternative, semi-parametric method for forecasting, which uses state-dependent weights derived from a kernel function. The forecasts that are obtained using this method are accurate and therefore potentially useful to electricity retailers in terms of risk management.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Estimating benefits of energy storage for aggregate storage applications in electricity distribution networks in Queensland

Electric Energy Storage (EES) is considered as one of the promising options for reducing the need for costly upgrades in distribution networks in Queensland (QLD). However, It is expected, the full potential for storage for distribution upgrade deferral cannot be fully realized due to high cost of EES. On the other hand, EES used for distribution deferral application can support a variety of complementary storage applications such as energy price arbitrage, time of use (TOU) energy cost reduction, wholesale electricity market ancillary services, and transmission upgrade deferral. Aggregation of benefits of these complementary storage applications would have the potential for increasing the amount of EES that may be financially attractive to defer distribution network augmentation in QLD. In this context, this paper analyzes distribution upgrade deferral, energy price arbitrage, TOU energy cost reduction, and integrated solar PV-storage benefits of EES devices in QLD.

Defining expected cost for the air conditioning to avoid a price spike of electricity market under DSR model

This work presents a demand side response model (DSR) which assists small electricity consumers, through an aggregator, exposed to the market price to proactively mitigate price and peak impact on the electrical system. The proposed model allows consumers to manage air-conditioning when as a function of possible price spikes. The main contribution of this research is to demonstrate how consumers can minimise the total expected cost by optimising air-conditioning to account for occurrences of a price spike in the electricity market. This model investigates how pre-cooling method can be used to minimise energy costs when there is a substantial risk of an electricity price spike. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics during hot days on weekdays in the period 2011 to 2012.

‘Redstone is like electricity’: Children's performative representations in and around Minecraft

In this article, we investigate eight and nine year old girls’ school and home use of the popular game Minecraft and the ways in which the girls ‘bring themselves into being’ through talk and digital production in the social spaces of the classroom and within the game’s multiplayer online world. This work was conducted as part of a broader digital games in education project involving primary and secondary school-aged students in Australia and focuses specifically on data collected from an all-girls primary school in Brisbane. We investigate the processes of identity construction that occur as the girls undertake practices of curatorship (Potter, 2012) to display their knowledge of Minecraft through discussion of the game, both ‘in world’ and in face-to-face interactions, and as they assemble resources within and around the game to design, build and display their creations and share stories about their game play. The article begins with a consideration of recent scholarship focussing on children, learning and digital culture and literacy practices before explaining how Minecraft is, in many ways, an exemplary instance of a digital game that promotes and enables complex practices of digital participation. We then introduce the concepts of performativity and recognition (Butler 1990, 2004, 2005) which, we argue, provide productive ways to theorise identity work within affinity groups. The article then outlines some background to the research project and our methodology before providing analysis of the data in the second half of the article. We conclude by outlining the implications of our investigation for the conceptualisation of learning spaces as affinity groups and for considering digital participation as curatorship.

Moving from outsider to insider : peer status and partnerships between electricity utilities and residential consumers

An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008. By 2011, both the peak demand and grid supplied electricity consumption had decreased to below pre-intervention levels. This case study research explored the relationship developed between the utility, community and individual consumer from the residential customer perspective through qualitative research of 22 residential households. It is proposed that an energy utility can be highly successful at peak demand reduction by becoming a community member and a peer to residential consumers and developing the necessary trust, access, influence and partnership required to create the responsive environment to change. A peer-community approach could provide policymakers with a pathway for implementing pro-environmental behaviour for low carbon communities, as well as peak demand reduction, thereby addressing government emission targets while limiting the cost of living increases from infrastructure expenditure.