Regional Environmental Change

, Volume 14, Issue 2, pp 463–473 | Cite as

The challenge of adapting centralised electricity systems: peak demand and maladaptation in South East Queensland, Australia

  • George Quezada
  • George Grozev
  • Seongwon Seo
  • Chi-Hsiang Wang
Open Access
Original Article

Abstract

South East Queensland’s (SEQ’s) centralised electricity system is under great pressure to adapt. Climate change is converging with socio-economic, demographic and technological changes to create a ‘perfect storm’ for the region’s electricity system. Distribution networks are particularly affected, with these factors contributing to tremendous peak demand growth, about double the rate of growth in average demand in recent years. This paper reviews how Australia’s electricity system is adapting to multiple drivers of peak electricity demand. We use socio-technical transitions theory to understand the temporal interconnected social and technical dimensions of adaptation in this setting. Specifically, we present an historical narrative of the emergence of centralisation in Australia and outline the peak demand problem in SEQ and review adaptation options from the international literature. We also analyse the interactions between key social groups and their adaptation responses over the past decade. Our analysis shows that adaptation has become a contested process between supply-chain actors and end-users, each with different economic objectives, adaptation needs and capacities. The resulting adaptation dynamic that is emerging shows worrying signs of maladaptation. Implications for market governance and urban policy and research are discussed.

Keywords

Adaptation Socio-technical transitions Peak electricity demand Electricity networks 

Introduction

The technology transitions literature has gained prominence in recent years, particularly in addressing complex sustainability issues (Elzen and Wieczorek 2005; Smith et al. 2010). Drawing on the sociology of technology and evolutionary economics, this field deals with the issue of human agency in technological development, and the multi-layered interactions that have led to historically significant technological shifts within societal functions, such as mobility, sanitation and energy supply. This area has yielded valuable insights into the processes and patterns of change of large infrastructural systems, including centralised electricity systems (e.g., van der Vleuten and Raven 2006; Verbong and Geels 2007). Following this tradition, electricity systems are conceptualised as evolving ‘socio-technical systems’ (STSs); products of a ‘seamless web’ of interacting social, cultural, physical and institutional elements (Hughes 1987).

Centralised electricity systems around the world face unprecedented challenges: changing climate, shifting settlement patterns and socio-economic and demographic changes and technological developments are posing complex adaptation dilemmas for policy makers and utilities. Climate change-related extreme weather can cause outages (Willbanks et al. 2007; Shahid 2012), reduce system efficiency on hot days and increase demand due to wide use of air-conditioners, most particularly in warmer climates (Miller et al. 2008; Giannakopoulos et al. 2011). Continued urbanisation and economic growth are increasingly associated with energy-intensive lifestyles (Madlener and Sunak 2011; Dhakal 2009; Martinez-Zarzoso and Maruotti 2011; Kim and Barles 2012). Globally, these lifestyles are being fed by more affordable mass-produced high electrical demand appliances, such as air-conditioners and flat-screen televisions, forcing governments and utilities to build more infrastructure to keep up with demand. Electric vehicles are poised to add further demand pressure (Higgins et al. 2012).

While directly impacting on security of supply and consumption, the above factors are also driving up electricity demand peaks, which are short periods of elevated electricity use that stem from different seasonal and daily patterns (depending on climate zone). For example, early evening peaks are characteristic in summer afternoons of warm regions. Recent evidence from North America and Australia identifies rapid diffusion and high penetration of residential air-conditioners as the main source of the problem in these regions (e.g., Garnaut 2011; Newsham and Bowker 2010). This paper addresses the question of adapting centralised electricity systems in response to interconnected factors that are driving increased peak demand.

Adaptation often refers to a set of responses to alter, adjust or modify human, technical and economic activities in response to actual or expected climatic stimuli and impacts (Smit et al. 2000). However, as indicated above, adaptation to climate change is situated in a broader landscape of factors (e.g., socio-economic, geographic, demographic and technological trends) that impact on the electricity sector. The problem of peak demand is one such emerging impact. We contextualise this problem within the region of South East Queensland (SEQ), Australia, and draw on socio-technical transitions theory to understand the temporal multi-level, multi-factor and multi-actor dynamics of adaptation in this setting. Specifically, we employ the multi-level perspective (MLP, Geels 2002; Rip and Kemp 1998) as an analytical lens.

The MLP is a process theory that conceptualises STS change as an emergent phenomenon of three interacting levels of the system: socio-technical regimes (meso level), landscapes (macro level) and niche innovations (micro level). Regimes are dominant and stable configurations of physical, social and institutional elements which constitute a socio-technical system (e.g., centralised electricity supply) and are characterised by lock-in dynamics as elements within regimes are ‘aligned and coordinated’ based on legacy infrastructure/technology, policies, guiding principles, user practices, industry rules and regulations and organisational routines that develop over long time periods. Niche innovations refer to small-scale networks of actors, which are distinct from regimes in being loosely coordinated relationships based on trial-and-error experimentation with novel technologies and configurations (Kemp et al. 1998). The landscape level consists of slowly changing exogenous factors such as climate change, population growth, resource availability and cultural values; influential factors that impact on the interplay between regime and niche levels, but are beyond the control of individual actors.

According to the MLP, incumbent regimes can undergo shifts or transitions when landscape pressures (e.g., climate change) and technical problems (among others) weaken the alignment between regime elements, thus opening ‘windows of opportunity’ for new configurations or niche innovations to change previously set institutions, physical artefacts, user practices etc. (Geels 2004). These periods of opportunity can catalyse change in regimes, which over multiple decades can bring about change from one STS to another. However, the course of regime change is often a contested process as actors become uncoordinated with differing priorities, goals and visions, which open multiple possible futures for the STS (Brown et al. 2000; Smith et al. 2005).

Steep increases in peak demand is one recent technical problem that has gained widespread attention in Australia. The literature cites a number of adaptation options for managing peak demand (Vine 2012), including building new electricity generation and network infrastructure, direct control of air-conditioners and other electricity ‘hungry’ devices during peak periods (Newsham 2011; Reddy 1991), introducing new time-of-use electricity tariffs (Newsham and Bowker 2010), educating consumers to shift demand and improving housing/household energy efficiency (Vine 2012).

However, adapting effectively requires an understanding of how such options are enacted by different groups of actors in the system, responding to different stimuli, with different adaptation needs and capacities (see Keys et al. 2013); and how these interactions impact on the system as a whole. For example, end-users are more concerned about amenity from electricity (e.g., thermal comfort) than the problem of peak demand. For end-users in Australia, adopting refrigerant air-conditioners may be a valid adaptation response to climate change-related increases in temperature (see Hadley Centre 2011), but a poor response from an electricity network perspective as it increases peak demand, network costs and ultimately electricity prices, which disproportionately impact on low-income consumer groups (Farbotko and Waitt 2011). This is because network operators tend to build grid capacity to cater for the relatively short-lived peak events that are driven by air-conditioner use, and this approach decreases average asset utilisation, placing an upward pressure on electricity prices. Such a scenario increases the vulnerability of low-income groups to fuel poverty and extreme heat events predicted under climate change modelling for Australia (see CSIRO 2007). This interplay and resulting dynamic between actors illustrates a key adaptation risk—actions carried out by different groups of actors in a complex socio-technical system can lead to maladaptation (Barnett and O’Neill 2010; Burton 1997).

This paper adds to the literature by analysing the historical socio-technical factors that bring about maladaptive responses in vast and complex infrastructural systems, like centralised electricity systems. In bringing together STSs perspectives with an evaluation of adaptation actions, we highlight dilemmas and unintended consequences associated with economic policy, electricity industry deregulation, and more recently reform under climate change policy.

Adaptation options and the evolving centralised electricity paradigm

As a process theory, the MLP is concerned with patterns and processes of change over long periods of time. In this section, we are concerned with how adaptation of the centralised electricity system in SEQ is situated within the industry’s historical context and the interplay with important landscape factors, and how these play out across scales—national as well as regional. SEQ’s peak demand problem, available adaptation options and the system’s history therefore constitute the context of our later application of the MLP approach on the past decade of adaptation.

The peak demand problem in South East Queensland and possible management strategies

SEQ is a relatively small sub-tropical region within the Queensland state government jurisdiction, mainly located between 26° S and 28° S latitude. It spans an area of 22,890 square kilometres and is highly urbanised with most of its 3 million inhabitants located in the major centres of Brisbane (Queensland’s capital), Gold Coast, Sunshine Coast, Ipswich and Toowoomba (ABS 2011). As a fast growing region in Australia, SEQ has a well-documented peak demand problem (Queensland Government 2008). According to the region’s distributor (www.energex.com.au), 13 % of capacity is only used for a few hours, a few times a year. One recent study on peak demand in the region was undertaken as part of the SEQ Climate Adaptation Research Initiative (Wang et al. 2012). In summary, this study gathered and analysed demand data for a period of 10 years (2002–2011) based on half-hour time series data obtained from Energex. Peak annual demand growth was estimated at 4.1 %, about double the rate of overall annual demand growth (2.2 %) (see Fig. 1). Rising use of air-conditioners coinciding with other residential loads (in the early evening) has been identified as a major driver of peak demand growth. Based on historical and simulated projections, Wang et al. (2012) suggested that annual peak electricity demand will continue to grow independently from average demand.
Fig. 1

Annual peak and average electricity demands in the region of SEQ, Australia

Various strategies for managing peak demand are cited in the literature. Newsham and Bowker (2010) reviewed several pilot projects for direct load control in North America and concluded that reductions between 0.3 and 1.2 kW per air-conditioning unit could be achieved. A number of North American studies have also examined the impact of dynamic tariffs structures on peak electricity reductions (Newsham and Bowker 2010; Faruqui and Sergici 2010). Based on reviews of previous studies, Newsham and Bowker (2010) found that critical peak pricing (applied on a small number of event days advertised by the utility company a day in advance) is the most effective strategy from this class, achieving 30 % load reduction compared to 5 % for time-of-use tariffs. A less direct approach to shaping customer usage patterns is through education and feedback (Vine 2012), though no research has evaluated the impact on peak demand. Building energy efficiency has received widespread attention as a cost-effective strategy for energy (and emissions) reduction (Miller et al. 2012) that can also reduce demand peaks (Steinfeld et al. 2011). This option incorporates passive solar design, cross ventilation, shading and building orientation, and also appropriate building materials, facades and colours. Steinfeld et al. (2011) analysed peak load characteristics of Sydney office buildings and found that peak loads for office buildings with best practice energy performance were 26 % lower than for buildings with average energy performance, while the total annual energy consumption was 57 % lower.

Historical review: regional and national-scale analysis, major landscape developments and the emerging centralised electricity regime

The development of a centralised electricity system in Australia and the SEQ region emerged from the interplay of major social, economic, political and technological changes that occurred throughout the OECD during the twentieth century. The electricity industry was established in the late nineteenth century with many small utilities based in colonial town centres, and evolved and developed into a national-scale and nationally regulated sector over a period of more than 100 years (Booth 2003). The colonial centres became capital cities within six states and two territories (referred to in this paper as states for simplicity) after federation in 1901. Early electricity utilities were niche innovations in the context of a broader regime for stationery energy, typically dominated by wood fuel for heating and cooking, and kerosene, gas and candles for lighting. Electricity suppliers were vertically integrated companies owned privately or by local government municipalities, typically supplying electricity to nearby customers, street lighting and tramways (Sharma 2003). One of SEQ’s first utilities was the Brisbane Tramway Company, which supplied electricity to Brisbane’s inner city commercial and industrial customers and the railways; later the company was the first to supply electricity to suburban customers in SEQ, following the adoption of more efficient alternating current generators (Simmers 2004).

From a market perspective, the early decades of the twentieth century were a highly contested period for Brisbane’s burgeoning electricity industry, characterised by competition for customers and between private and public (local government) interests (Simmers 2004). The industry was loosely regulated by the state government, which held powers to grant permission for electricity companies to build network infrastructure to new customers. Electricity was expensive, but demand for electric lighting was substantial due to perceived superiority (convenience, safety, reduced heat) over traditional lighting. This demand prompted investment in progressively larger coal-fired generator sets (Simmers 2004), enabling local electricity systems to emerge as a new regime for lighting. Demand continued to grow with the diffusion of electrical appliances and industrial machinery, while technical improvements and economies-of-scale in the electricity supply-chain reduced retail prices. In time, electricity became symbolised as a basic need. State governments soon recognised the political appeal of supplying electricity and began enacting legislation to own electricity supply assets (Sharma 2003).

State ownership was all but complete by the late 1940s (Sharma 2003). Each state jurisdiction operated vertically integrated monopolies with centralised planning and operation (Moran 2008; Sharma and Bartels 1997). States concerned themselves with problem agendas regarding urban air quality, electricity prices and service reliability and established regulated standards, which guided utility planners towards a hub-and-spoke infrastructure model. As dominant actors, states also pursued their own economic objectives (jobs, use of state resources) and ensured independence from other states (Sharma 2003). In time fewer, larger and more remote generators were linked to customers, irrespective of load size, distance from a generator and network infrastructure costs. Queensland’s network in particular became more dispersed than any other state as demand grew with rising living standards (Wadley 1981).

In the 1950s, Australia (along with much of the world) entered a prolonged economic boom lasting until the early 1970s. Growth was brought about by a combination of landscape factors stemming from widespread acceptance of Keynesian economic policy, immigration policy and population growth, shifting employment from agriculture to manufacturing and services, and urban policy that promoted home ownership and low density suburban development of outer metropolitan areas (Berry 1999). During this time, city-regions began competing for investment and accommodated the needs of both labour markets and capital through highway enabled suburban sprawl (Harvey 1989; McCarty 1970). Urban expansionist policy in the second half of the century became particularly evident along SEQ’s coastal strip, as the region became the fastest growing in Australia, and small towns in the Gold Coast and Sunshine Coast merged with Brisbane through an apparent sea of housing estates (Spearritt 2009).

Growth in the electricity network necessarily followed and increases in supply capacity accelerated to accommodate the proliferation and diffusion of electrical appliances and growing consumer desire for greater comfort (Shove 2003; Schipper 1987); electricity now provided for wants as well as needs. Seasonal and daily electricity demand peaks emerged as heating and cooling appliances became more common, while pronounced daily peaks can be traced to a combination of spatial segregation of residential areas (dormitory suburbs) from other uses (e.g., commercial and industrial) (Schnore 1957) and increasing participation of women in the workforce (Evans and Kelley 2008), shifting most household electricity consumption outside of normal working hours.

Towards the latter part of the century, the global economic policy landscape shifted to less government involvement and more open, competitive and deregulated markets. Concerns about the efficiency and international competitiveness of the sector were evident at the federal level during the 80s and early 90s (Moran 2008; Sharma 2003). Political momentum continued to build leading to the eventual establishment the National Electricity Market—a wholesale spot market facilitated by a single grid linking all eastern Australian states, including Queensland. A federal competition agenda was also pursued leading to progressive disaggregation of the electricity supply-chain and privatisation of both electricity generation and retail businesses. This reform was intended to bring about a competitive market, delivering long-term benefits to consumers with respect to price, quality and reliability as codified in the National Electricity Law. Today, national institutions govern the system, overseeing investment in centralised infrastructure (Australian Energy Regulator) and managing market access, operation and market planning (Australian Energy Market Operator).

Application of the MLP to adaptation of centralised electricity systems

Thus far, we have shown how landscape developments related to population and economic growth, rising affluence (and consumer values) and urban sprawl produced an electricity regime based on fewer large coal-fired generators and vast transmission and distribution networks. The evolution of centralisation was also linked to the changing dominance of regime actors and formalisation of rules and regulations; from small private and local government companies loosely governed by states, to state owned and governed monopolies, through to a federally governed industry of private and state owned companies. Lock-in effects became evident with increasing investment and formalisation of rules and processes. On the demand side, changing social practices and urbanisation patterns drove up electricity consumption, particularly of a peaky nature. The response adopted by regime actors to this problem has been largely directed towards augmenting and reinforcing the network, increasing system costs. However, in recent years, evidence has grown regarding the adaptation value of demand-side management options, underscoring a growing awareness within the regime for the need to change investment rules and practices in the electricity sector.

Australia’s electricity regime entered a new era at the turn of this century, with the roll-out of various climate change policy measures supporting demand-side management and renewable energy. These policies arose in the context of additional landscape pressures, allowing windows of opportunity for niche innovations based on decentralised (small-scale) electricity technologies and supply configurations, and causing previously coordinated and aligned regime actors to pursue divergent adaptation strategies. In this section, we present a narrative of these adaptation dynamics, drawing on the MLP and illustrating the indications of maladaptation and the prospect for two emerging and contested futures for the centralised grid.

The latter part of the twentieth century and early years of the twenty-first century saw historic droughts, floods, heat waves and fires across Australia, which impacted on the political mood towards environmental issues (Gascoigne 2008). Global shifts in political mood regarding anthropogenic climate change also impacted on Australian politics. These landscape changes resulted in new energy policies to promote renewable energy, energy efficiency and demand management. Diffusion of refrigerant air-conditioners also reached the take-off phase at this time, surging from 35 % in 2000 to an estimated 70 % today (EES 2006) and placing significant strain on residential distribution systems. The dramatic penetration of residential air-conditioners was arguably an adaptation response to historically high summer temperatures in Australia (see Hadley Centre 2011), and a combination of more affordable air-conditioners with relatively poor thermal performance of Australian housing (see Horne and Hayles 2008). Distributors responded by making significant investments in additional network infrastructure (AEMC 2010; Garnaut 2011). For example, about AUD$45 billion is scheduled for network investment across Australia for the current planning period (July 2010–June 2015), with about one-third of that investment for peak demand growth alone (Dunstan et al. 2011). In SEQ, Energex received approval from the energy regulator to invest AUD$6.4 billion for the current period, a 58 % increase from the previous 5-year period. Previous increases in network spending resulted in dramatic electricity price rises, in the order of 30 % in the 2007–2010 period; a result that is expected to be replicated as locked-in network investments for the current determination period flow through to future retail pricing determinations (AEMC 2010). Queensland’s economic regulator recently approved electricity price rises, which will effectively raise electricity bills for the average residential customer in 2013–2014 by more than 20 % compared to the preceding year (QCA 2013).

To counter increasing electricity costs, some end-users took advantage of recent photovoltaic (PV) technology developments and favourable climate change policies. Specifically, more affordable mass-produced PV systems from China and government incentives for PV enabled typically middle–high-income households (Bruce et al. 2009) to effectively hedge against electricity price rises. A federal PV rebate was established in 2000 to encourage small end-users (typically households, small businesses and community groups) to invest in grid-connected renewable energy. Federal and state governments introduced rebates for solar hot water and heat pump systems as well. This was followed by a raft of further incentives, including a federal renewable energy target underpinned by a renewable energy certificates market, and notably, state-based feed-in tariffs (FiTs) for small grid-connected PV systems (typically <5 kW). Queensland’s net FiT was among the highest in the country at 44 cents/kWh, or about double the retail electricity price in SEQ, which allowed some PV owners to generate an income from the excess electricity they produced.

These conditions drove meteoric growth in the PV market, with over 500,000 systems installed across Australia, or over 1,031 MW capacity by 2011 (up from 0.4 MW in 2000 and 111.9 MW in 2009; CEC 2011). The growth reduced the economic cost of PV to grid parity for residential systems in 2012 (APVA 2011). Regime actors became concerned about the impact of high PV penetration on grid stability and social equity, and the influence of generous FiTs on electricity prices (e.g., Nelson et al. 2012). This prompted most state governments to remove or vastly reduce FiTs in the last couple of years. In July 2012, Queensland’s FiT was reduced to 8 c/kWhr as part of a suite of policy changes to address factors driving up electricity prices.

Climate change politics also shifted investment practices in the large-scale generation sector, as financiers factored in carbon pricing and a carbon constrained future. Climate change policy and pricing carbon dioxide emissions were debated during the 2007 federal election. This and other environmental issues arguably resulted in the ousting of the incumbent conservative government, and election of Kevin Rudd’s pro-carbon pricing Labour government (Gascoigne 2008). Prime Minister Rudd signed the Kyoto Protocol soon after the election signalling the government’s intent to set emission reduction targets for Australia. Consequently, more investment flowed to more expensive but lower emission gas generators for peaking and base–load power, particularly in Queensland, where significant coal seam gas resources were identified (Simshauser et al. 2011a). Towards the end of 2011, the Australian Government passed laws to price carbon, commencing in July 2012 with a carbon tax set at $23/tonne.

In terms of demand management and energy efficiency, regime actors at both the federal and state government level were active in delivering programmes to reduce emissions, address peak demand and the burden of mounting electricity prices. The Australian Government implemented minimum energy performance standards for major appliances. In 2008, an insulation rebate was rolled out as part of the government’s stimulus package during the global financial crisis. (However, this programme was discontinued amid concerns regarding the programme’s implementation.) Other federal programmes included smart grid trials, and financing mechanisms and agencies to fund renewable energy and energy efficiency. In SEQ, Energex initiated remote demand management trials, while the Queensland Government established an energy audit programme for households and businesses, which provided education and advice on how to reduce electricity costs.

Early signs of policy success emerged in 2010, with the National Electricity Market registering a modest fall in consumption of 212 MWh (0.1 %), the first in its history, followed by a further fall of 1,836 GWh (1.0 %) in 2011, and a sharp fall of 4,660 GWh (2.4 %) in 2012 (AEMO 2012). These falls were brought about by a combination of economic factors (declining manufacturing activity due to high Australian dollar, and lower than expected domestic economic growth) and notably, significant penetration of small-scale PV and demand-side curtailment (AEMO 2012). AEMO’s latest forecast is for modest annual demand growth of 1.7 % for the 2010–2020 period.

Early signs of maladaptation: combining the multi-level perspective with an adaptation evaluation framework

Historical analysis of the emergence of electricity centralisation in Australia shows that electricity demand and peak demand are products of complex multi-level interactions. At the landscape level, government policy trends related to population and economic growth and urban expansionism shaped a supply-oriented electricity sector. An electricity regime based on large generators and vast transmission and distribution systems developed with the aim of achieving economies-of-scale in a market growth context. State governments became attracted to the notion of supplying essential services through infrastructure ownership, and end-users progressively aligned with this regime by enacting practices that became increasingly energy intensive. Aspirations shifted towards greater levels of comfort in response to economic/urban growth and consumer-capitalist policies; a process enabled by an increasingly abundant supply of cheap mass-produced electrical goods and growing household income.

Amidst this historical interplay between the landscape and regime levels, recent natural disasters and the introduction of climate change policy disrupted and weakened the alignment between regime actors. Resulting adaptation patterns across Australia’s electricity system reveal a tension between supply-chain actors and end-users (see Fig. 2). Supply-chain actors have typically employed an adaptation strategy within the dominant centralisation paradigm (Dosi 1982), whereby innovation (e.g., direct demand management and smart grids/redundancy etc.) is aimed at preserving profitability and viability of legacy assets and organisational competencies, as well as meeting regulated standards for safety, reliability and security of supply. Many end-users or consumers, used to meeting needs and wants with increasingly affordable electrical gadgets, have chosen to adapt to elevated summer temperatures by actively managing thermal comfort with high demand refrigerant air-conditioners, and some have responded to electricity cost consequences through energy independence measures (e.g., installing small-scale PV systems). Clean energy and energy efficiency policy measures are reducing demand as well, which illustrates how momentum for climate change mitigation is placing pressure on the regime to fundamentally change.
Fig. 2

Signs of maladaptation emerging from different economic objectives and divergent adaptation strategies between electricity supply-chain actors and end-users

Unfortunately, this dynamic is leading to early signs of maladaptation. Barnett and O’Neill (2010) defined 5 types of maladaptation based on observed water infrastructure adaptation in a large urban region of Australia that was responding to severe drought. The responses they cite led to the following: increased greenhouse gas emissions, disproportionate burden on the most vulnerable, high opportunity costs and reduced incentive to adapt and path dependency. In the case of Australia’s electricity system, vulnerable groups such as low-income households are beginning to experience fuel poverty as system-wide costs escalate (Simshauser et al. 2011b), and path dependency appears likely to continue as further investments flow to centralised assets to solve problems related to peak demand and high penetration of PV.

Furthermore, the roll-out of PV and energy conservation strategies in response to rising electricity prices could develop into a technological substitution transition pathway (Geels and Schot 2007), where niche innovations (and networks) in energy services and small-scale electricity generation gain momentum and progressively replace the centralised electricity regime, resulting in stranded assets, power struggles and regime defence strategies (aka. sailing ship effects: see supply-chain adaptation strategies in Fig. 2). Social inequity risks becoming more severe over time as the vicious cycle of electricity price rises to pay for centralised assets in a declining market fuel further market development for small-scale local energy solutions. Such a scenario will drive niche-accumulation (Geels and Schot 2007), or improvements in price/performance ratios of small-scale generators relative to central grid electricity, which is likely to further erode market share from the National Electricity Market. For example, the cost of solar electricity systems is expected to continue declining (APVA 2011), and the emerging market for electric vehicles is projected to dramatically reduce the cost of battery storage over the next decade (Narula et al. 2011; Hensley et al. 2012). Such developments will improve cost/performance ratios of off-grid systems, perhaps to a point where these systems are more cost-effective than grid electricity for more and more end-users, particularly at the grid’s fringe (SKM 2011).

Discussion

The aim of this paper was to address the question of adapting centralised electricity systems to climate change and other exogenous factors pushing up peak demand. We contextualised our analysis within SEQ and Australia and used the MLP (Geels 2002; Rip and Kemp 1998) as an analytical lens to understand how centralisation evolved and how adaptation has proceeded in this setting. This analysis indicated some early signs of maladaptation of the system, as outlined by Barnett and O’Neill (2010). While not detailing the reasons why maladaptation occurs, Barnett and O’Neill (2010) cite the time-lag between climate change and institutional change as a key factor. In this paper, we add that maladaptation arises in a complex socio-technical system where different groups of actors behave in economically rational ways and employ divergent adaptation strategies in response to multi-factor and multi-level interplay.

Applying the MLP helped situate recent adaptation by regime actors in terms of historical (landscape) factors and processes that shaped lock-in effects and triggered windows of opportunity. Early development of the electricity industry saw state governments seize control of electricity supply and become dominant actors to drive state-centric economic development. Investment and engineering practices favoured centralisation under guiding principles of economic growth and social equity. User practices initially reflected the view of electricity as a basic need, but then shifted to ‘wants’ as government sponsored economic and urban growth made housing, electricity and electrical appliances more available. Increasing scale of infrastructure (and investment) coupled with demand growth produced a lock-in spiral, which accelerated with a move to national-level governance, integration of state systems into a larger network (and market), disaggregation of the supply-chain and further formalisation of rules and regulations. Windows of opportunity have opened recently for niche innovations in distributed energy systems and strategies (i.e., micro-renewables, energy efficiency and demand management), in response to two emerging landscape pressures: (1) peak demand-investment coupling producing steep rises in electricity prices, and (2) climate change and related policy incentives for renewable energy and energy efficiency.

Consequently, electricity supply in Australia appears set to transition, albeit with tension in the foreseeable future between divergent technological paradigms and trajectories. One trajectory, led by supply-chain actors, follows continual improvement and reinforcement of the legacy centralised system, with augmentation focussed on addressing problems associated with peak demand and grid-connected micro-generators (a super grid); and another involves end-users, typically affluent, reducing reliance on the central grid as relative price/performance ratios for micro-generators and off-grid systems improve (an off-grid revolution), thus reducing market demand and revenue for the super grid.

Policy makers in Australia face the complex task of managing the transition from fossil fuel–based centralisation in a growing electricity market to partial decentralisation in a stabilising or declining market with a growing proportion of electricity from renewable sources. Such a transition has been envisioned and discussed by others in relation to severe landscape pressures, such as disruption in fuel supply or supply-chain vulnerability to climate change impacts (Blokhuis et al. 2011; Bouffard and Kirschen 2008; Verbong and Geels 2010). Our analysis also highlights the central role of climate change policy in driving greater decentralisation and lower demand.

We suggest the multi-level dynamics of maladaptation outlined above hold important implications for market governance and economic policy. The first implication is pragmatic and concerns the process for signalling investment in new generation and network infrastructure. The second relates to broader issues of addressing long-term economic and urban policy.

Firstly, despite three consecutive years of market falls, Australia’s market operator persists with forecasting market growth, albeit modest in their ‘low scenario’, which will continue to drive investment in new infrastructure. This infrastructure will face high risks of becoming stranded or underutilised, contributing to the linked problems of steep rising retail electricity prices and energy poverty. The current investment process is insensitive to market disruptions due to new technology and climate change policy. Policy makers, regulators and industry need to consider new investment protocols that evaluate grid versus non-grid investments on the basis of adaptation effectiveness, which includes emission reduction objectives, but goes beyond to consider social equity concerns and the risk of exacerbating path dependency in the sector. Further research is needed to explore new market designs and develop modelling tools and methods to underpin such investment protocols.

The second implication suggests that addressing the risk of maladaptation will require situating climate change policy in the context of historical momentum for economic growth and urban expansionism and related social transformation (i.e., rising affluent lifestyles). Recent economic reform agendas have focussed on notions of decoupling economic growth from consumption of resources, in this case shifting the energy market based on profit from electricity throughput towards an energy services paradigm (Steinberger et al. 2009). Recent attempts to decouple utility incentives appear to be partial and inadequate (Kihm 2009), and some authors express concern about the prospect of rebound effects associated with technological and market design improvements, particularly given the prevailing consumer-capitalist society (Herring and Roy 2007; Trainer 2011). While wholesale changes to economic policy are unlikely, the urban policy realm may hold some promise for addressing these concerns.

Much has been written about the potential role of eco-developments and cities in seeding new configurations for production and consumption systems (e.g., Bulkeley et al. 2010; Romero-Lankao and Dodman 2011; Swilling 2011). Szatow et al. (2012) recently discussed the central role of property sector actors in driving cleaner and more efficient energy supply configurations. As outlined in this paper, urban development patterns in Australia have been a key contributor to driving energy demand and peak demand issues emerge from segregated land uses. Addressing peak demand and related electricity prices rises effectively involves moving away from segregating land uses towards mixed-uses, thus improving network utilisation. In terms of addressing consumption, some also point to the value of urban consolidation—a form of rationing—which involves reducing dwelling and lot size (Clune et al. 2012). Urban consolidation and densification appears to be a logical response and offers benefits in terms of using existing infrastructure more efficiently. However, the energy transition described in this paper is one of energy quality—from high quality (coal) to low (renewable) in terms of energy return. Some authors raise questions about urban densification in the context of an energy system based on low-gain renewable energy (Hagan 2012; Hui 2001; Tainter et al. 2003). Research is needed to examine the relationship between urban density and renewable energy supply and to identify optimal urban planning models that would harmonise with more renewable and decentralised electricity supply configurations.

From a governance perspective, undertaking this urban research will require unique institutional arrangements across land use and infrastructure regimes. For example, the Queensland Government has land use and infrastructure planning powers for certain state designated urban development areas, including new satellite cities in SEQ. This sort of institutional innovation offers substantial opportunities to explore new energy supply regimes that can effectively and equitably address climate change mitigation and adaptation policy objectives. We consider this research endeavour urgent given the long timeframes involved in transitioning urban areas and associated infrastructural systems.

Notes

Acknowledgments

We like to thank Ryan McAllister, Michael Kane and Anthony Szatow for their constructive feedback during the preparation of this manuscript. Thanks also for the helpful suggestions of two anonymous reviewers. We are grateful to Energex for access to electricity demand data and for many constructive discussions during several meetings with ENERGEX specialists. This study is partially supported by the South East Queensland Climate Adaptation Research Initiative, a partnership between the Queensland and Australian Governments, the CSIRO Climate Adaptation National Research Flagship, Griffith University, University of the Sunshine Coast and The University of Queensland. The Initiative aims to provide research knowledge to enable the region to adapt and prepare for the impacts of climate change.

References

  1. ABS (2011) Regional population growth, Australia, 2009–2010. Canberra, AustraliaGoogle Scholar
  2. AEMC (2010) Future possible retail electricity price movements: 1 July 2010 to 30 June 2013, Final report. Sydney, NSWGoogle Scholar
  3. AEMO (2012) National electricity forecasting report for the national electricity market (NEM). Australian energy market operatorGoogle Scholar
  4. APVA (2011) Modelling of PV and electricity prices in the Australian residential sector, 2011. Australian PV associationGoogle Scholar
  5. Barnett J, O’Neill S (2010) Maladaptation. Global Environ Change 20(2):211–213. doi:10.1016/j.gloenvcha.2009.11.004 CrossRefGoogle Scholar
  6. Berry M (1999) Unravelling the “Australian housing solution”: the post-war years. Hous Theory Soc 16(3):106–123. doi:10.1080/14036099950149974 CrossRefGoogle Scholar
  7. Blokhuis E, Brouwers B, van der Putten E, Schaefer W (2011) Peak loads and network investments in sustainable energy transitions. Energy Policy 39(10):6220CrossRefGoogle Scholar
  8. Booth RR (2003) Warring tribes: the story of power development in Australia, 2nd edn. Bardak Group, West PerthGoogle Scholar
  9. Bouffard F, Kirschen DS (2008) Centralised and distributed electricity systems. Energy Policy 36(12):4504–4508. doi:10.1016/j.enpol.2008.09.060 CrossRefGoogle Scholar
  10. Brown N, Rappert B, Webster A (eds) (2000) Contested futures: a sociology of prospective techno-science. Ashgate Publishing Limited, AldershotGoogle Scholar
  11. Bruce A, Watt ME, Passey R (2009) Who buys PV systems? A survey of NSW residential PV rebate recipients. Paper presented at the Solar09, the 47th ANZSES annual conference, Townsville, Queensland, AustraliaGoogle Scholar
  12. Bulkeley H, Broto CB, Hodson M, Marvin S (eds) (2010) Cities and low carbon transitions. Routledge, LondonGoogle Scholar
  13. Burton I (1997) Vulnerability and adaptive response in the context of climate and climate change. Clim Change 36(1–2):185–196. doi:10.1023/a:1005334926618 CrossRefGoogle Scholar
  14. CEC (2011) Clean energy Australia report 2011. Clean Energy CouncilGoogle Scholar
  15. Centre Hadley (2011) Climate: observations, projections and impacts—Australia. The Met Office Hadley Centre, ExeterGoogle Scholar
  16. Clune S, Morrissey J, Moore T (2012) Size matters: house size and thermal efficiency as policy strategies to reduce net emissions of new developments. Energy Policy (0). doi:10.1016/j.enpol.2012.05.072
  17. CSIRO (2007) Climate change in Australia—technical report 2007. CSIRO, Bureau of Meteorology, Australian Government, CanberraGoogle Scholar
  18. Dhakal S (2009) Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy 37(11):4208–4219CrossRefGoogle Scholar
  19. Dosi G (1982) Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change. Res Policy 11(3):147–162. doi:10.1016/0048-7333(82)90016-6 CrossRefGoogle Scholar
  20. Dunstan C, Boronyak L, Langham L, Ison N, Usher J, Cooper C, White S (2011) Think small: the Australian decentralised energy roadmap: Issue 1, December 2011. CSIRO Intelligent Grid Research Program. Institute for Sustainable Futures, University of Technology SydneyGoogle Scholar
  21. EES (2006) Status of air conditioners in Australia—Updated with 2005 data. ACT, CanberraGoogle Scholar
  22. Elzen B, Wieczorek A (2005) Transitions towards sustainability through system innovation. Technol Forecast Soc Change 72(6):651–661. doi:10.1016/j.techfore.2005.04.002 CrossRefGoogle Scholar
  23. Evans MDR, Kelley J (2008) Trends in women’s labor force participation in Australia: 1984–2002. Soc Sci Res 37(1):287–310. doi:10.1016/j.ssresearch.2007.01.009 CrossRefGoogle Scholar
  24. Farbotko C, Waitt G (2011) Residential air-conditioning and climate change: voices of the vulnerable. Health Promot J Aust 22:S13–S16Google Scholar
  25. Faruqui A, Sergici S (2010) Household response to dynamic pricing of electricity: a survey of 15 experiments. J Regul Econ 38(2):193–225. doi:10.1007/s11149-010-9127-y CrossRefGoogle Scholar
  26. Garnaut R (2011) Garnaut climate change review—update paper eight: transforming the electricity sector. Commonwealth of Australia, CanberraGoogle Scholar
  27. Gascoigne T (2008) Climate change: “The” issue in the Australian national election 2007? Sci Commun 29(4):522–531. doi:10.1177/1075547008316306 CrossRefGoogle Scholar
  28. Geels FW (2002) Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Res Policy 31(8–9):1257–1274. doi:10.1016/s0048-7333(02)00062-8 CrossRefGoogle Scholar
  29. Geels FW (2004) From sectoral systems of innovation to socio-technical systems: insights about dynamics and change from sociology and institutional theory. Res Policy 33(6–7):897–920. doi:10.1016/j.respol.2004.01.015 CrossRefGoogle Scholar
  30. Geels FW, Schot J (2007) Typology of sociotechnical transition pathways. Res Policy 36(3):399–417CrossRefGoogle Scholar
  31. Giannakopoulos C, Kostopoulou E, Varotsos K, Tziotziou K, Plitharas A (2011) An integrated assessment of climate change impacts for Greece in the near future. Reg Environ Change 11(4):829–843. doi:10.1007/s10113-011-0219-8 CrossRefGoogle Scholar
  32. Hagan S (2012) Metabolic suburbs or the virtue of low densities. Arch Res Q 16(1):9–13. doi:10.1017/S1359135512000243 Google Scholar
  33. Harvey D (1989) From managerialism to entrepreneurialism: the transformation in urban governance in late capitalism. Geogr Ann Ser B 71(1):3–17. doi:10.2307/490503 CrossRefGoogle Scholar
  34. Hensley R, Newman J, Rogers M (2012) Battery technology charges ahead. McKinsey Quarterly JulyGoogle Scholar
  35. Herring H, Roy R (2007) Technological innovation, energy efficient design and the rebound effect. Technovation 27(4):194–203CrossRefGoogle Scholar
  36. Higgins A, Paevere P, Gardner J, Quezada G (2012) Combining choice modelling and multi-criteria analysis for technology diffusion: an application to the uptake of electric vehicles. Technol Forecast Soc Change 79:1399–1412CrossRefGoogle Scholar
  37. Horne R, Hayles C (2008) Towards global benchmarking for sustainable homes: an international comparison of the energy performance of housing. J Hous Built Environ 23(2):119–130. doi:10.1007/s10901-008-9105-1 CrossRefGoogle Scholar
  38. Hughes TP (1987) The evolution of large technological systems. In: Bijker WE, Hughes TP, Pinch T (eds) The social construction of technological systems: new directions in the sociology and history of technology. MIT Press, Cambridge, pp 51–82Google Scholar
  39. Hui SCM (2001) Low energy building design in high density urban cities. Renew Energy 24(3–4):627–640. doi:10.1016/s0960-1481(01)00049-0 CrossRefGoogle Scholar
  40. Kemp R, Schot J, Hoogma R (1998) Regime shifts to sustainability through processes of niche formation: the approach of strategic niche management. Technol Anal Strateg Manag 10(2):175–195CrossRefGoogle Scholar
  41. Keys N, Bussey M, Thomsen DC, Lynam T, Smith TF (2013) Building adaptive capacity in South East Queensland, Australia. Reg Environ Change. doi:10.1007/s10113-012-0394-2 Google Scholar
  42. Kihm S (2009) When revenue decoupling will work … and when it won’t. Electr J 22(8):19–28. doi:10.1016/j.tej.2009.08.002 CrossRefGoogle Scholar
  43. Kim E, Barles S (2012) The energy consumption of Paris and its supply areas from the eighteenth century to the present. Reg Environ Change 12(2):295–310. doi:10.1007/s10113-011-0275-0 CrossRefGoogle Scholar
  44. Madlener R, Sunak Y (2011) Impacts of urbanization on urban structures and energy demand: what can we learn for urban energy planning and urbanization management? Sustain Cities Soc 1(1):45–53. doi:10.1016/j.scs.2010.08.006 CrossRefGoogle Scholar
  45. Martinez-Zarzoso I, Maruotti A (2011) The impact of urbanization on CO2 emissions: evidence from developing countries. Ecol Econ 70(7):1344–1353CrossRefGoogle Scholar
  46. McCarty JW (1970) Australian capital cities in the 19th century. Aust Econ Hist Rev 10(2):107Google Scholar
  47. Miller NL, Hayhoe K, Jin J, Auffhammer M (2008) Climate, extreme heat, and electricity demand in California. J Appl Meteorol Climatol 47(6):1834–1844. doi:10.1175/2007jamc1480.1 CrossRefGoogle Scholar
  48. Miller W, Buys L, Bell J (2012) Performance evaluation of eight contemporary passive solar homes in subtropical Australia. Build Environ 56(2):57–68CrossRefGoogle Scholar
  49. Moran A (2008) The emergence of Australia’s electricity market. Int J Global Energy Issues 29(1–2):88–108. doi:10.1504/ijgei.2008.016343 CrossRefGoogle Scholar
  50. Narula CK, Martinez R, Onar O, Starke MR, Andrews G (2011) Economic analysis of deploying used batteries in power systems. Oak Ridge National Laboratory, Oak RidgeGoogle Scholar
  51. Nelson T, Simshauser P, Nelson J (2012) Queensland solar feed-in tariffs and the merit-order effect: economic benefit, or regressive taxation and wealth transfers? AGL Appl Econ Policy Res, working paper no 30:1–22Google Scholar
  52. Newsham G (2011) A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use. Energy Policy 39(10):6376CrossRefGoogle Scholar
  53. Newsham GR, Bowker BG (2010) The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: a review. Energy Policy 38(7):3289–3296. doi:10.1016/j.enpol.2010.01.027 CrossRefGoogle Scholar
  54. QCA (2013) Draft determination—regulated retail electricity prices 2013–14. Queensland Competition Authority, BrisbaneGoogle Scholar
  55. Queensland Government (2008) South East Queensland: state of the region. Technical report. Queensland Government, BrisbaneGoogle Scholar
  56. Reddy TA (1991) Shaving residential air-conditioner electricity peaks by intelligent use of the building thermal mass. Energy (Oxford) 16(7):1001CrossRefGoogle Scholar
  57. Rip A, Kemp R (1998) Technological change. human choice and climate change, Volume 2: resources and technology, Battelle Press, 2:327–327Google Scholar
  58. Romero-Lankao P, Dodman D (2011) Cities in transition: transforming urban centers from hotbeds of GHG emissions and vulnerability to seedbeds of sustainability and resilience: introduction and Editorial overview. Current Opin Environ Sustain 3(3):113–120. doi:10.1016/j.cosust.2011.02.002 CrossRefGoogle Scholar
  59. Schipper L (1987) Residential electricity consumption in industrialized countries: changes since 1973. Energy (Oxford) 12(12):1197CrossRefGoogle Scholar
  60. Schnore LF (1957) The growth of metropolitan suburbs. Am Soc Rev 22(2):165CrossRefGoogle Scholar
  61. Shahid S (2012) Vulnerability of the power sector of Bangladesh to climate change and extreme weather events. Reg Environ Change 12(3):595–606. doi:10.1007/s10113-011-0276-z CrossRefGoogle Scholar
  62. Sharma D (2003) The multidimensionality of electricity reform-an Australian perspective. Energy Policy 31(11):1093CrossRefGoogle Scholar
  63. Sharma D, Bartels R (1997) Distributed electricity generation in competitive energy markets: A case study in Australia. Energy J 19 (SPEC. ISS.):17–39Google Scholar
  64. Shove E (2003) Users, technologies and expectations of comfort, cleanliness and convenience. Innovation 16(2):193–206Google Scholar
  65. Simmers JM (2004) The coming of the light to suburban Brisbane. Aust J Electr Electron Eng 1(2):127–141Google Scholar
  66. Simshauser P, Nelson T, Doan T (2011a) The boomerang paradox, part I: how a nation’s wealth is creating fuel poverty. Electr J 24(1):72–91. doi:10.1016/j.tej.2010.12.001 CrossRefGoogle Scholar
  67. Simshauser P, Nelson T, Doan T (2011b) The boomerang paradox, part II: policy prescriptions for reducing fuel poverty in Australia. Electr J 24(2):63–75. doi:10.1016/j.tej.2011.01.017 CrossRefGoogle Scholar
  68. SKM (2011) Preliminary assessment of stand alone power systems as an alternative to grid connections at the fringe of the grid. Alternative Technology Association, MelbourneGoogle Scholar
  69. Smit B, Burton I, Klein RJT, Wandel J (2000) An anatomy of adaptation to climate change and variability. Clim Change 45(1):223CrossRefGoogle Scholar
  70. Smith A, Stirling A, Berkhout F (2005) The governance of sustainable socio-technical transitions. Res Policy 34(10):1491–1510. doi:10.1016/j.respol.2005.07.005 CrossRefGoogle Scholar
  71. Smith A, Voß J-P, Grin J (2010) Innovation studies and sustainability transitions: the allure of the multi-level perspective and its challenges. Res Policy 39(4):435–448CrossRefGoogle Scholar
  72. Spearritt P (2009) The 200 km city: Brisbane, the gold coast, and sunshine coast. Aust Econ Hist Rev 49(1):87–106CrossRefGoogle Scholar
  73. Steinberger JK, van Niel J, Bourg D (2009) Profiting from negawatts: reducing absolute consumption and emissions through a performance-based energy economy. Energy Policy 37(1):361–370CrossRefGoogle Scholar
  74. Steinfeld J, Bruce A, Watt M (2011) Peak load characteristics of Sydney office buildings and policy recommendations for peak load reduction. Energy Build 43(9):2179–2187CrossRefGoogle Scholar
  75. Swilling M (2011) Reconceptualising urbanism, ecology and networked infrastructures. Soc Dyn-J Cent Afr Stud Univ Cape Town 37(1):78–95Google Scholar
  76. Szatow A, Quezada G, Lilley B (2012) New light on an old problem: reflections on barriers and enablers of distributed energy. Energy Policy 43:1–5. doi:10.1016/j.enpol.2011.07.057 CrossRefGoogle Scholar
  77. Tainter JA, Little A, Allen TF, Hoekstra TW (2003) Resource transitions and energy gain: contexts of organization. Conserv Ecol 7(3):4. http://www.consecol.org/vol7/iss3/art4 Google Scholar
  78. Trainer T (2011) We can’t have our cake and eat it too: why the energy and climate problems cannot be solved in consumer-capitalist society. Energy, Sustainability and the Environment: Technology, Incentives, Behaviour. Butterworth-Heinemann, AmsterdamGoogle Scholar
  79. van der Vleuten E, Raven R (2006) Lock-in and change: distributed generation in Denmark in a long-term perspective. Energy Policy 34(18):3739–3748. doi:10.1016/j.enpol.2005.08.016 CrossRefGoogle Scholar
  80. Verbong G, Geels F (2007) The ongoing energy transition: lessons from a socio-technical, multi-level analysis of the Dutch electricity system (1960–2004). Energy Policy 35(2):1025–1037. doi:10.1016/j.enpol.2006.02.010 CrossRefGoogle Scholar
  81. Verbong GPJ, Geels FW (2010) Exploring sustainability transitions in the electricity sector with socio-technical pathways. Technol Forecast Soc Change 77(8):1214–1221. doi:10.1016/j.techfore.2010.04.008 CrossRefGoogle Scholar
  82. Vine E (2012) Adaptation of California’s electricity sector to climate change. Clim Change 111(1):75–99. doi:10.1007/s10584-011-0242-2 CrossRefGoogle Scholar
  83. Wadley D (1981) Cost, price, and revenue differentials in electricity supply: Queensland and Australia. Aust Geogr Stud 19(1):25–46CrossRefGoogle Scholar
  84. Wang C-h, Grozev G, Seo S (2012) Decomposition and statistical analysis for regional electricity demand forecasting. Energy 41(1):313–325. doi:10.1016/j.energy.2012.03.011 CrossRefGoogle Scholar
  85. Willbanks TJ, Lankao PR, Bao M, Berkhout F, Cairncross S, Ceron JP, Kapshe M, Muir-Wood R, Zapata-Marti R (2007) Industry, settlement and society. Climate change 2007: Impacts adaptation and vulnerability. contribution of working group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar

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© The Author(s) 2013

Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • George Quezada
    • 1
  • George Grozev
    • 2
  • Seongwon Seo
    • 2
  • Chi-Hsiang Wang
    • 2
  1. 1.Social and Economic Sciences ProgramCSIRO Ecosystem SciencesBrisbaneAustralia
  2. 2.Urban Systems ProgramCSIRO Ecosystem SciencesBrisbaneAustralia

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