When will it be cost effective for. consumers to disconnect from central. grids for distributed generation in. Australia?

When will it be cost effective for consumers to disconnect from central grids for distributed generation in Australia? Ben Humphreys, BEng (Mech), PGDip Energy Studies. School of Engineering and Energy Murdoch University, Perth, WA PEC624 Masters of Science in Renewable Energy Dissertation 2013 Page 1 of 79

Executive summary Rapid and significant reductions in the costs of solar photovoltaic technology and considerable rises in grid electricity prices have prompted the question: When will it be cost effective for consumers to disconnect from central grids for distributed generation in Australia? This report seeks to give insight into this question by comparing standalone solar PV and storage system levelised cost forecasts to centralised grid electricity price forecasts. The report also, based on literature research, discusses the impacts and issues associated with increased distributed generation in general on the current electricity market and incumbent utilities. The future costs of Photovoltaic (PV) with storage systems were forecast using single factor experience curves. The Homer energy modelling software was then used to optimise the systems and calculate the levelised cost of electricity (LCOE) for each scenario. Future electricity prices were estimated based on data from AEMOc (2012). The analysis treats the two as independent variables; therefore, it does not account for interrelationships that exist in reality. Consequently, the results are best viewed as a range of possible outcomes that, given the breadth of the range covered, are likely to include the actual outcome. The analysis focuses on small consumers such as residential, and small to medium businesses, because it was thought that this consumer group was most likely to have premises suitable for a PV system that met most of their electricity consumption. This consumer group consumes a significant amount of Australia s total electricity: estimated at between 30 and 50 percent based on data from IEA (2012) and AEMO (2010). The analysis considered multiple scenarios: grid electricity against standalone PV with storage, and grid electricity against 50 percent and 75 percent PV penetration levels. All scenarios considered low electricity price states with an average price of $0.30/kWh and high price states with an average price of $0.40/kWh. The analysis revealed that solar PV with storage could be competitive with the grid in supplying 50 to 75 percent of a small consumer s electricity demand within the short term (<5 years), and on a standalone basis in the medium term (<10 years). It is expected that this will lead to reduced demand for grid electricity, falling revenues for incumbent utilities and, therefore, a negative impact on their profitability. As a result of falling demand, network service providers will likely need to raise per unit Page 2 of 79

charges in order to recover the revenue required to meet their regulated return on asset base. This will drive electricity prices higher, thus increasing the competitiveness of distributed generation (DG), and other technologies such as energy efficiency and energy management for that matter. In turn, consumers demand for grid electricity will reduce, and the uptake of DG will increase, which will reduce the networks ability to recover revenue further. Because of falling demand, generators face lower wholesale prices, compressed margins, and the risk of stranded assets. This scenario of falling demand and rising prices is commonly referred in the literature as the death spiral (Kind, 2013; Nelson & Simshauser, 2012; Newbury, 2013; Severance, 2011). A review of literature identified DG as being potentially disruptive to the existing electricity market. Based on case studies of disruptive technologies in other industries, DG can be expected to cause significant market changes and create significant risks for incumbents, especially those in monopoly situations. Interview based research in Australia and Germany suggests that most incumbent electricity utilities are not well prepared to handle competition from DG, are slow in realising the threat to their business model, and are failing to see the market opportunities. Government intervention is likely given the significant amount of recent government and industry reports on the topic; this will influence the uptake of DG technologies, and subsequently the time it will take for them to become competitive at high penetration levels or on a standalone basis. Some recommended changes such as removal of price regulation, time of use pricing, demand pricing, and integrated network planning that includes DG would tend to increase the uptake of DG technologies, subsequently driving down DG costs faster. However, other recommended changes such as high fixed charges, additional fixed charges specifically for PV owners, and penetration limitations by network service providers would act to slow the uptake and cost reductions of DG. While intervention is likely, what intervention is difficult to say with confidence at this point in time given that the recent change of Federal Government has put in doubt the relevance of existing government literature on the topic. In addition, the crowded energy policy space often changes policy significantly between white/inquiry paper and enactment. Page 3 of 79

Acknowledgments The author would like to thank Adam McHugh, Murdoch University for assistance and guidance, Geodynamics Limited staff for providing informed opinions, and Geodynamics Limited for allowing study leave. Page 4 of 79

Contents Executive summary... 2 Acknowledgments... 4 1 Introduction... 7 2 Background... 9 2.1 Centralised generation... 9 2.2 Networks and rising electricity prices... 10 2.3 Distributed generation... 12 2.4 The rapid uptake of PV... 14 2.5 Background summary and research question... 15 3 Methodology... 16 3.1 The concept of experience curves... 16 3.2 Current and future electricity costs... 19 3.3 Solar photovoltaic module costs... 21 3.3.1 Historical trends... 21 3.3.2 Forecasts... 24 3.4 Battery storage costs... 27 3.4.1 Historical costs... 27 3.4.2 Forecasts... 31 3.5 Balance of system costs... 36 3.6 Homer Modelling... 37 3.6.1 Model creation... 37 3.6.2 Component cost assumptions... 39 3.6.3 Financial assumptions... 39 3.6.4 Solar radiation and orientation assumptions... 39 3.6.5 PV module performance assumptions... 40 3.6.6 Battery performance assumptions... 41 3.6.7 Load Profile assumptions... 42 3.6.8 Sensitivity analysis... 46 4 Analysis... 48 4.1 Centralised grid vs standalone PV with battery storage... 48 4.1.1 Results... 48 4.1.2 Discussion... 49 4.2 Grid connected PV with battery storage systems at high penetration levels... 52 4.2.1 Results... 52 Page 5 of 79

4.2.2 Discussion... 53 5 Discussion on impacts on electricity markets... 58 5.1 The concept of disruptive technologies... 58 5.2 The impact of disruptive innovation on incumbent utilities... 60 5.3 Market intervention... 61 6 Conclusions... 65 7 References... 67 8 Appendix A Homer Model Inputs... 71 9 Appendix B PV Module Data Sheet... 79 Page 6 of 79

1 Introduction Rapid and significant reductions in the costs of distributed generation technologies (predominatly PV), and considerable rises in grid electricity prices has prompted the question: When will it be cost effective for consumers to disconnect from central grids for distributed generation in Australia? Since 2008, Australia has seen soaring electricity prices and increasing media attention to the topic (Daily_Telegraph, 2012; Nolan, 2012; Novak, 2010; Solarbuzz, 2012; Wood, 2013). The majority of Australian states have seen retail electricity price rises of between 40 percent and 120 percent over the last 5 years (AER, 2012). Strong media coverage has raised public awareness of the issue, and this combined with government energy efficiency and education programs has influenced many consumers to seek ways to reduce electricity consumption and save money. Following global trends, attractive government incentives and significant cost reductions in solar photovoltaic (PV) systems have led many consumers in Australia to install PV to reduce grid consumption. In Australia, the cumulative installed capacity of PV systems has risen by an average of 120 percent per year for the last five years (Watt, Passey, & Johnston, 2011, 2012). Over one gigawatt (GW) of PV was installed in 2012 alone, which represents about two percent of Australia s total electricity generating capacity (Solarbuzz, 2012). The residential sector accounted for ~90 percent of these installations (Solarbuzz, 2012). The cost of generation from grid connected PV is now considered to be equivalent to, or less than, the purchase price of electricity for many residential and small business consumers (Solarbuzz, 2012). The energy storage industry currently bears striking similarities to the PV industry of five to ten years ago: Germany has begun an energy storage subsidy program, there is much commentary on the benefits and potential applications of energy storage, and numerous companies have been established within Australia developing and selling storage products. Also, energy storage technologies, particularly batteries, lend themselves to mass production; as such, huge installation growth and cost reductions in energy storage batteries, like have been observed in the PV industry, are a very real possibility. With the cost of PV continuing to fall, potentially large cost reductions in energy storage, and the price of grid electricity continuing to rise, it is reasonable to hypothesise that a PV system combined with Page 7 of 79

storage might, within the foreseeable future, be able to meet some consumers entire electricity demand at a lower levelised cost than the price of grid supplied electricity. This topic is important because it has the potential to have significant impacts on the current electricity market. In particular, this scenario presents major concerns for transmission and distribution network service providers (TNSPs and DNSPs), and generators, particularly those with large base load power stations, that have invested significant capital in long life assets based on forecast demand growth for grid delivered electricity. This is because the expected demand growth is under threat from distributed generation technologies, such as PV, and subsequently their ability to recover a return on their investment may be compromised. Falling demand for network service providers means that higher electricity prices are required for the same amount of revenue to be recovered. This provides a greater incentive for consumers to reduce demand further, potentially to the point of disconnecting from the grid when cost reductions in storage and self-generation make this possible. Generators are equally concerned by a falling demand scenario which reduces the wholesale price for electricity, and potentially limits their ability to recover investments in some generation capacity, simply because it is no longer required. This scenario of increasing per unit costs and decreasing demand is commonly referred to as the death spiral (Kind, 2013; Nelson & Simshauser, 2012; Newbury, 2013; Severance, 2011). The issue is further complicated by the fact that many network service providers and generators are large state government owned entities meaning there are potentially negative consequences for state budgets. The aim of this report is to investigate when it will be cost effective for residential and small business consumers to disconnect from central grids for distributed generation in Australia to meet their electricity needs, and consider the potential impacts on the current electricity market. While the report is focused on PV with battery storage, other distributed generation technologies such as fuel cells or micro generators, wind or micro hydro (in the right environment), energy management, energy efficiency, some unforeseeable technological breakthrough, or any combination of these, could provide consumers with an alternative to grid supplied electricity. Page 8 of 79

2 Background This section provides some definitions, explanations and background on the issue. 2.1 Centralised generation Centralised generation and network transmission and distribution is the prevailing method of electricity supply in Australia, and around the world for that matter. A centralised generation system refers to an electricity system that is centrally controlled and typically consists of large scale generators built close to fuel sources or delivery points such as ports, and long transmission and distribution networks (networks) that deliver the electricity to consumers (Boyle, Everett, & Ramage, 2004). These large networks cover thousands of kilometres and connect multiple generators to millions of consumers. Such systems brought significant economies of scale, efficiency and standardisation to the supply of electricity (Boyle et al., 2004). The electricity market in Australia includes the National Electricity Market (NEM) which accounts for ~90 percent of Australia s electricity consumption (AER, 2009). The NEM is a wholesale electricity market that trades more than $10 billion of electricity per year, supplies ~8 million consumers (AEMO 2010), and physically joins all the eastern states: Queensland (QLD), New South Wales (NSW), Australian Capital Territory (ACT), Victoria (VIC), South Australia (SA), and Tasmania (TAS), into one large integrated electricity system. The other Australian states, Western Australia and the Northern Territory, operate independent electricity markets predominatly because of their remoteness to the eastern states and each other. Western Australia (WA) operates two larger electricity systems, the South West Interconnected System (SWIS) and the North West Interconnected System (NWIS), in addition to ~30 small regional networks (AER, 2009). The SWIS, which covers the most heavily populated area in WA, has a wholesale electricity market, which is called, appropriately, the Wholesale Electricity Market (WEM). The Northern Territory (NT) has three small networks: the Darwin Katherine, Alice Springs and Tennant Creek systems (AER, 2009). The industry is still largely government owned and operated as a planned system, and there is no wholesale electricity market (AER, 2009). The consumer cost of electricity provided by centralised generation consists of five major components: generation, transmission, distribution, retail, and taxes less subsidies. Figure 1 shows the contribution Page 9 of 79

of each component to the total cost. Importantly, it can be seen that transmission and distribution makes up ~45 percent of the total cost, while actually generating the electricity costs less than one third. Figure 1 Typical electricity price breakdown (AER 2012, Aurora Energy (2013)) 2.2 Networks and rising electricity prices Transmission and distribution networks are considered natural monopolies. A natural monopoly arises when it is more efficient (i.e. lowest long run average cost) to allow one firm to supply the market rather than have multiple firms competing (Economics_Online, 2013). Typical characteristics of natural monopolies are significant economies of scale, large upfront capital costs, low operating costs, and declining average costs as output increases (AER, 2012; Economics_Online, 2013). There are fundamental issues with natural monopolies as they are incentivised to produce at a quantity to maximise profit that results in an allocative inefficiency. As stated by Garnaut (2010); Where infrastructure is best provided by a single firm, the firm may, without competition or regulation, underprovide and overcharge for use of the infrastructure. As a result, natural monopolies are normally government owned, or privately owned with substantial government regulation, to prevent misuse of the monopoly power (Economics_Online, 2013). However, even with substantial regulation, the naturally monopolistic characteristics of transmission and distribution networks can lead to economically inefficient market outcomes. To explain, in Page 10 of 79

Australia networks are regulated to receive revenue based on a set rate of return on the value of their asset base (Mountain, 2011), which strongly incentivises the network to continually increase the value of its asset base leading to over investment or gold plating (Sioshansi, 2006). (Mountain, 2011) suggests that an inefficient response to demand growth, by networks, has contributed to recent excessive electricity price increases in Australia. In addition, there is little incentive for networks to innovate and utilise more cost effective solutions to demand growth, such as distributed generation or demand side management, because they operate in a low risk environment with no competition. The last ~5 years has seen electricity prices soar in Australia, which has led Australia s retail electricity prices to be among the highest of OECD countries (IEA 2012). Figure 3 shows a graph of the rise in prices; noteworthy is the accelerated price growth from 2007 to 2011. AER (2012) states that rising network costs (predominatly distribution networks) are the main reason for the price increase, while green schemes, generation costs, and retail costs contributed to a lesser extent. A case study for NSW is shown in Figure 3. Figure 2 Electricity price indices for Australian households and businesses, 1981 2011 (Select_Committee_on_Electricity_Prices, 2012) Page 11 of 79

Figure 3 Breakdown of increases in electricity prices in NSW (AER, 2012) 2.3 Distributed generation Distributed generation (DG) is a term used to describe the application of power generating equipment at, or near, the load, and usually refers to smaller systems rather than the large on site generation plants seen in energy intensive industry applications. Embedded or decentralised generation are common terms also used to describe DG. There are considerable variations on the criteria used to define DG (Pepermans 2005). Pepermans (2005) suggests that the best definition is that the connection of the generation equipment is directly to the distribution network, or on the customer side of the meter. The general definition used for this report is that of the Australian Energy Market Operator (AEMOd, 2012). As shown schematically in Figure 4, is generation installed by customers, including, for example, some relatively large generators that may be located on customer premises, back-up generators that rarely run, roof-top photovoltaics, micro generation from fuel cells, landfill generators, small cogeneration, and very small wind farms. (AEMOd, 2012). Note that for the purposes of the study the scope is limited to distribution customers. DG technologies are not new and they are used for a wide variety of applications. In fact, the first power plants and grids were small DG systems with storage (Pepermans 2005). Since the rise of centralised generation, DG technologies have been most commonly used in applications where it is cost prohibitive or impractical for grid connection such as: space, remote or temporary communications and lighting, remote or isolated locations, oil and gas drilling, and mining. Page 12 of 79

Figure 4 Overview of electricity network in the NEM (AEMO 2012) Page 13 of 79

2.4 The rapid uptake of PV The last ~10 years has seen the global solar PV market significantly increase in terms of installed capacity and market value (see Figure 5). In Australia, PV installations have grown most significantly over the last four to five years as shown by Figure 6. This rapid uptake has led to large cost reductions, and now the generation cost of PV is equal to, or lower than, retail electricity prices in Australia as shown in Figure 7 (Solarbuzz 2012). AEMO (2012) predicted that installed PV capacity would reach between about three and eight GW by 2020, which is between six and sixteen percent of the total current generation capacity. Figure 5 Annual capacity investment in various renewables 2001-2011 (IEA, 2012) Figure 6 Australian installed capacity and growth rates 2008 2011 (Solarbuzz, 2012) Page 14 of 79

Figure 7 Comparison of cost of generation from Solar PV to electricity prices (Solarbuzz, 2012). This graph displays Solarbuzz s view of the levelised cost of generation from PV compared to the location specific electricity tariffs. The bars represent the LCOE of PV in each state; the value of which is how much greater the PV LCOE is than the local residential retail grid tariff. If a bar is underneath the dotted line, then PV is has a lower LCOE than the grid tariff in that state. 2.5 Background summary and research question The previous sections identified the following key points: Centralised generation has become quite expensive and transmission and distribution costs are large component of the overall costs. Solar PV systems have reduced dramatically in price in recent times and can now generate electricity cheaper than the grid tariff for many consumers. This report investigates when PV with battery storage systems will be a cost effective alternative to grid supplied electricity for the average retail consumer. Page 15 of 79

3 Methodology The analysis is a comparison between forecast costs of solar PV and battery storage technologies using the concept of experience curves against the estimated future costs of centralised electricity using published data (AEMOc, 2012). The Homer energy modelling software was used to optimise the system and calculate the levelised cost of electricity (LCOE) for each scenario. Future electricity price rises, as estimated by the National Institute of Economic and Industry Research (NIEIR), were taken from AEMOc (2012). Previous forecasts were not available to gauge reliability. However, other credible sources were researched and they published similar forecasts (AEMC, 2011; Roam_Consulting, 2012; SKM_MMA, 2011). The consumer was assumed to be an average household consuming 20 kwh/day, with a peak demand of 4 kw and some demand management, in Brisbane, Queensland. However, the household could just as easily be in any Australian state, and the general findings are relevant to all of Australia. Obviously, solar radiation varies from place to place, but it is not the dominant factor (proven in Section 3.6.8). Capital costs of equipment and competing electricity prices are the main influences with regard to whether the cost of PV with storage generation is competitive with grid electricity (proven in Section 3.6.8). Likewise, the consumer could be any size, or could be a small business, as the analysis is on a per kwh basis. Obviously, this assumes that the PV system required can physically fit at the premise and there is little or no shading. The analysis uses weighted average grid electricity unit prices that included fixed supply charges as opposed to a specific grid tariff structure, which was deemed to be a suitable for comparing grid connected versus standalone systems. No effort has been made to describe the operating principles or characteristics of the technologies mentioned. This information is available in a wide range of literature and those seeking such information are directed to Fthenakis and Nikolakakis (2012), Akhil et al. (2013), Kazmerski (2012), and El-Khattam and Salama (2004). 3.1 The concept of experience curves Experience curves or learning curves are a widely accepted method of predicting future technology costs based on the cumulative quantity of production, or in this case installed capacity (Cottrell et al., 2003; Hayward, Graham, & Campbell, 2011). Experience curves have been in use since the 1960s and were developed from the learning curve concept which dates back to the 1930s when Wright observed that the cost of producing military aircraft declined at a more or less constant rate for each Page 16 of 79

doubling of aircraft produced. (Hayward et al., 2011). Arrow (1962) may have started the concept with his paper entitled; The Economic Implications of Learning by Doing. Grübler et al (1999) discusses in detail the incorporation of technology learning in broad based energy economic models, which up until that point was not common. The difference in meaning between experience curves and learning curves is not always obvious in the literature which can lead to confusion. Urfer, Scaife, and Wibberley (2004) describe learning curves as company specific which traditionally include only labour costs; whereas, experience curves cover whole industries, or technologies, and include all associated costs. In this paper the term experience curve will be used to describe whole technologies inclusive of all costs. Experience curves are often calculated as follows (Urfer et al., 2004): Equation 1 Note X t means installed capacity at year t. Other commonly used terms are the progress ratio which indicates the reduction in price due to doubling of cumulative capacity, and the related learning rate (Urfer et al., 2004): Equation 2 Equation 3 There is much literature on single factor experience curves being too simple for accurately predicting future costs of electricity generation. This is because actual changes in cost are due to the interaction of a range of complex factors inside and outside the learning process that are difficult to forecast: research and development spending and success; policy changes; input cost changes (i.e. fuel, materials, externalities, etc.); technological breakthroughs; and market behaviour and/or market structural change (IEA, 2000). It should also be noted that cost changes do not necessarily directly relate to price changes (IEA, 2000). Page 17 of 79

As a result many studies have focused on improving the accuracy of experience curves by using more complicated equations to account for research and development (Gomez, 2001), and learning and non-learning components (Ferioli, Schoots, & van der Zwaan, 2009). However, it should be remembered that learning curves were developed from empirical observations and are heavily influenced by things that cannot be accurately forecast. Because of this, single factor experience curves, with consideration of important specific factors and uncertainties, were considered appropriate. Some of these important factors are worthy of further mention: Time frame experience curves are a tool best used for the prediction of technological learning over the long term (> 10 years); short to medium term (2-10 years) predictions are treated with additional uncertainty. To account for this a broad range of learning rates were used to attain a boundary around the most likely outcomes. Different learning rates for different components of the one technology for example, significant learning rates have been observed in the production of PV modules, ~20 percent; however, balance of plant components have only experienced learning rates of ~10 percent (ACIL-Tasman, 2008). Applying a learning rate to the generation of electricity is essentially applying an average learning rate to all the contributing activities which may lead to inaccuracies. This has been accepted as a reasonable simplification given the underlying uncertainty in forecasting. Global spillover this refers to countries benefiting from the technology learning acquired in other countries. For example, large reductions in the cost of PV modules has been predominatly due to the ability of the Chinese to manufacture them at very low costs, and because China exports these all over the world, other countries benefit from the learning acquired. However, not all technological learning, such as industry skill, is easily transferred between countries. In addition, there may be lags or incomplete transfer of learnings because some countries might be slow adopters, have additional import costs, have regulatory constraints, have a different market structure, or different competing technologies. As a result it is not correct to directly apply global learning rates to one specific country (Cottrell et al., 2003). According to (Cottrell et al., 2003) Cottrell et al. (2003) It is erroneous to take costs from different regions and apply locally with simply using a currency conversion due to the Page 18 of 79

differences in material/equip, labour costs, etc. As a result, where available, local data has been used to adjust the experience curves. Exchange rates exchange rates create uncertainty in applying global learning rates in a specific country as the cost of imported components are affected by exchange rates (Cottrell et al., 2003). For example, the cost of PV modules may be falling, but if the Australian dollar is also falling then the global learning is not captured locally. The complication with exchange rates is that only parts of the total system costs are affected, and that future exchange rates are extremely difficult to forecast. To account for this local costs have been used where possible. In addition, there are other factors that can have significant impacts on technological learning that are extremely difficult to forecast: cumulative capacity over time, government policy, R&D spending, technological breakthroughs, or market shakeout. These factors are not addressed specifically; but are deemed to be accounted for in the range of scenarios considered. 3.2 Current and future electricity costs As a standalone DG system competes with the total cost of grid electricity, a weighted average cost of grid electricity including fixed supply charges was calculated for the comparison. Published retail and small business electricity prices (AGL, 2013; Aurora Energy, 2013; NT_Power, 2013; Origin_Energy, 2013; Synergy, 2013) and an assumed typical 20 kwh/day load profile were used to calculate the average cost per kwh. Table 1 and Table 2 show the results. While the pricing structures were quite different for most states, it was found that the weighted average residential cost was quite similar ($0.27-$0.33/kWh). SA ($0.41/kWh), NT ($0.38/kWh), and ACT ($0.23/kWh) were the exceptions. The overall average total residential price was $0.32/kWh, but two distinct price brackets could be observed: low and high. With regard to small business, the price range was smaller between states ($0.04-$0.05/kWh). The average was higher at $0.35/kWh when assuming the same energy consumption. However, it could be argued that most small businesses would tend to use more, and so the calculations were repeated assuming 30,000 kwhpa consumption. This average was the same as residential prices at $0.32/kWh. From this analysis, two starting prices were taken for the standalone comparison: low price states with a starting price of $0.30/kWh, and high price states with a starting price of $0.40/kWh. Page 19 of 79

For the high penetration analysis (50% and 75% solar penetration), the same starting electricity prices were used. While it could be argued that only the lower variable rates should be used, the higher rates were maintained for the following reasons: the high penetration analysis does not allow grid export and therefore it could still represent a standalone system as the additional power could just as easily be from a dispatchable DG technology such as a fuel cell; and even if the DG system was connected to the grid, the energy management system is assumed to be configured to minimise grid consumption at higher peak prices, and in NSW peak prices are as high as $0.53/kWh (AGL, 2013). AEMOc (2012) stated that average electricity prices in the NEM were forecast to rise in real terms by five percent per annum in the short term, out to 2014/2015, and one percent per annum in the medium term in a medium economic growth scenario. Based on this, three electricity price scenarios were considered: low with one percent per annum real growth, medium with three percent per annum growth, and high with five percent per annum growth. Table 1 Comparison of residential electricity prices ACT WA QLD NSW VIC TAS NT SA Weighted Avg Variable ($/kwh) $0.20 $0.25 $0.25 $0.27 $0.27 $0.28 $0.28 $0.38 Fixed ($/day) $0.67 $0.42 $0.69 $0.86 $1.02 $0.99 $2.09 $0.69 Assumed annual consumption (kwh) Annual unit cost including fixed daily charge ($/kwh) Table 2 Comparison of small business electricity prices 7,300 7,300 7,300 7,300 7,300 7,300 7,300 7,300 $0.23 $0.27 $0.28 $0.31 $0.32 $0.33 $0.38 $0.41 ACT TAS VIC WA QLD SA NT NSW Weighted Avg Variable ($/kwh) na $0.29 $0.28 $0.30 $0.30 $0.32 $0.32 $0.32 Fixed ($/day) na $1.06 $1.62 $0.46 $0.67 $0.67 $0.81 $1.46 Assumed annual consumption (kwh) 7,300 7,300 7,300 7,300 7,300 7,300 7,300 7,300 Annual unit cost including fixed daily charge ($/kwh) na $0.34 $0.36 $0.32 $0.33 $0.35 $0.36 $0.39 Assumed annual consumption (kwh) 30,000 30,000 30,000 30,000 30,000 30,000 30,000 30,000 Annual unit cost including fixed daily charge ($/kwh) na $0.30 $0.30 $0.31 $0.31 $0.33 $0.33 $0.34 Notes for Table 1&2: 1. Information accessed from retailers websites, references: Origin Energy, AGL, NT Power, ACTEWAGL, Aurora Energy, Synergy Page 20 of 79

3.3 Solar photovoltaic module costs 3.3.1 Historical trends Figure 8 shows the price and installation trends of PV systems within Australia since 1993 in real AUD2013 terms (data taken from Watt et al. (2011) and Watt et al. (2012) and adjusted with 2.5% escalation). Prices were basically stable right up until 2008 when they began to fall markedly. This coincided with large increases in installation rates that were driven by generous policy incentives including upfront rebates at the federal level, and feed-in tariffs at a state level, and enhanced by the strength of the AUD as can be observed in Figure 9. Figure 10 shows the same data on a log-log scale compared to the long term global trend of PV installed prices again in AUD2013 terms. Two distinct trends can be observed: between 10 100 MW constant prices and very low learning rates (~2 percent) persisted, despite a 10 fold increase in installed capacity, much slower than the long term global trend; however, from 100 2,500 MW large price reductions and very high learning rates (~28 percent) can be observed, much greater than the long term global trend. Given that many government incentives have been significantly wound back and the AUD has fallen, one would expect the learning rate to come back to a lower long term average now. The constant prices and low learning rates observed up to 100 MW installed (pre-2003) are curious, but could be partly explained by exchange rates and prices not being cost reflective. PV prices were declining from 1993 to 1997 but then increased with a falling AUD. The lower AUD persisted until 2002-03 when a significant fall in PV price was observed. However, the AUD trended higher from 2004 to 2008 but the PV price remained fairly steady. It is suggested that this may be due to the price not being cost reflective. To explain further, in a market with limited competition, prices are often maintained at higher levels despite falling costs because suppliers can achieve above normal profits. The IEA (2000) describe this behaviour graphically (see Figure 11), and use the term Price Umbrella to describe the price protection that suppliers with limited competition have. This is inevitably followed by a shakeout, where more firms enter the market because of the above normal profits available, and competition reduces profit margins back to being cost reflective. Page 21 of 79

Figure 8 Australian annual PV installed capacity and prices over time [data from (Watt et al., 2011, 2012)] Figure 9 2011, 2012)] Australian installed PV prices vs exchange rate [data from (RBA; Watt et al., Page 22 of 79

Figure 10 Australia & World installed PV cost vs cumulative capacity [data from (IEA, 2000; Watt et al., 2011, 2012)] Figure 11 Price-cost relations for a new product (Boston Consulting Group in(iea, 2000)) Page 23 of 79

3.3.2 Forecasts Based on Figure 8 the starting assumption for forecasting installed PV system prices should be ~$3,000/kW. However, it should be mentioned that this is a typical mid-year price, and towards the end of 2012 quality systems were being installed for ~$2,500/kW (estimated unsubsidised equivalent). It also worth mentioning that, at the time of writing, prices as low as ~$1,600 to $2,000 per kw (estimated unsubsidised equivalent) were being advertised (Solar_Choice, 2013) for residential 5kWe systems, and industry contacts advised that some commercial sized PV systems were being installed for ~$2,000/kW (personal confidential communication). Whether these low prices are truly cost reflective and representative of long term trends suitable for forecasting is debatable. The AUD has significantly weakened over the first half of 2013 which will increase costs of imported PV components. In addition, incentivising policies have been significantly wound back, and there exists significant uncertainty surrounding energy policy in Australia, which may mean that some suppliers are attempting to reduce stock levels in a hurry. For the analysis, it is suffice to say that ~$3,000/kW might be too high as a starting point given that systems are already being advertised for as low as ~$1,600/kW, but ~$2,000/kW might be too low given the abnormally high AUD and generous policies are subsiding rapidly. As such, three starting points were assumed: ~$2,000/kW, ~$2,500/kW, and ~$3,000/kW. Forecasts for future installation rates in Australia were researched from various sources (See Figure 12). Unsurprisingly, industry proponents forecast the highest annual growth rates, with averages of up to 41 percent over the next 4 years (Solarbuzz, 2012), and the centralised grid operator (AEMOf, 2012) forecast the lowest growth rates, with averages as low as 2.6 percent over the next 7 years. A global benchmark from an independent consultant was included to provide perspective, which gave average growth rates of 18 percent (Navigant-Research, 2013b). This was taken to be a reasonable assumption for a medium growth scenario, but was rounded to 20 percent for simplicity. 10 percent either side of this was assumed to be low and high growth scenarios. Figure 13 shows that these scenarios forecast a cumulative installed PV capacity of between 5 and 20 GW by 2020. With regard to future learning rates, it was thought reasonable to assume that the very high learning rates of ~27.5 percent experienced in Australia over the last 5 years would not continue because significantly reduced government rebates and feedin tariffs have decreased investment returns, and Page 24 of 79

many solar players have gone broke (Jones, 2013) suggesting that recent price falls are unsustainable. Therefore, longer term average learning rates were used: 10 percent, 15 percent and 20 percent, for low, medium, and high learning scenarios respectively. The future installed price for PV was then forecast (see Figure 14) for the following cases: 1. Low growth and learning scenario assumes starting price of ~$3,000/kW, installation growth rate of 10 percent, and learning rate of 10 percent. 2. Medium growth and learning scenario assumes starting price of ~$2,500/kW, installation growth rate of 20 percent, and learning rate of 15 percent. 3. High growth and learning scenario assumes starting price of ~$2,000/kW, installation growth rate of 30 percent, and learning rate of 20 percent. The scenarios forecast installed prices between $1,100 and $2,700 per kw by 2020 (see Figure 14). Figure 12 Australian cumulative installed capacity PV forecasts (AEMOf, 2012; Martin, 2013; Solarbuzz, 2012) Page 25 of 79

Figure 13 Assumed Australian cumulative installed capacity PV forecasts Figure 14 Forecast Australian PV installed prices over time Page 26 of 79

3.4 Battery storage costs This section establishes the cost assumptions for battery energy storage used in the analysis. As mentioned, it was desired to treat energy storage as a technology group rather than as individual technologies in order to represent the true choice that a consumer has. However, this provided a challenge when using experience curves as lead acid batteries are considered mature technology with significant already installed capacity, while other batteries such as NaS and Zinc bromine are considered new technologies with limited installed capacity. Another difficulty encountered was the variety of applications for batteries and deciding whether other applications, such as handheld devices and electric cars, could be reasonably included in cumulative installation capacity estimates for forecasting future battery costs for energy storage. In order to account for these issues, batteries were analysed in two groups: standard lead acid batteries, and all other energy storage batteries. Lead acid batteries were considered a mature technology with lower growth, and total installed cumulative capacity estimates regardless of the application were used. In contrast, all other energy storage batteries were grouped, considered to be new technologies with high growth, and only considered cumulative capacity estimates for energy storage applications. 3.4.1 Historical costs Historical costs were analysed for lead acid batteries only. The reason for this was that limited data was available for the other technologies, and that it was deemed sufficient to use the lead acid data to determine appropriate long term learning rates for the newer storage technologies. Cumulative installations were estimated by using the size of the current market, determining an average growth rate since the 1850s, and then using published unit costs over time to estimate the installed capacity. Figure 15 shows the historical price of lead acid batteries over time, with the price of lead overlaid, both in USD2013 real terms (adjusted using an escalation rate of 2.5%). The graph shows that real prices of lead-acid batteries have fallen substantially over the last 60 or so years. What is interesting to note is that the price of lead acid batteries has continued to fall over the last 10 years even though the price of lead has increased by 4-5 times. This tends to suggest that the material cost contribution Page 27 of 79

to lead acid batteries is not a dominant effect and/or that significant technological learning is continuing to take place. Figure 16 shows the cost of lead acid batteries against cumulative installations in order to estimate a long term average learning rate. Figure 16 shows this in USD2013 and AUD2013 in real terms on log scale with a fitted power equation, and suggests that the long term learning rate is ~18 percent. However, like the trends in PV prices, two distinct trends can be observed. Figure 17 shows an early negative learning rate to about the mid-1980s, and then a high learning rate of ~23 percent until the present time. From the analysis, learning rates of between 15 and 25 percent could be reasonably assumed for other battery technologies. Comparison with the learning rates established for PV indicates that these are appropriate learning rates for those technologies that lend themselves to mass production. It should be noted that it was difficult to find consistent and complete data, and published credible work on learning curves, for lead acid batteries. The application of the battery from which the data was published was not identified, which may give misleading trends, for example automotive batteries may have a different learning curve to deep cycle batteries. Published lead acid battery costs also varied quite significantly, which is no surprise given the huge range of applications, but it was difficult to analyse price changes over time. As a result the accuracy of the data points, including installed capacity, is questionable. That being said, the analysis has produced sensible results: a long term learning rate suitable for a technology that lends itself to mass production, and a price that accurately reflects the current price. As a result, it was deemed to be a suitable base assumption for forecasting. Therefore learning rate assumptions for lead acid and new technology batteries were based on the long term learning rate determined for lead acid batteries of 18 percent. Page 28 of 79

Figure 15 Price of lead acid batteries vs lead (Investment-Mine, 2013; Nagy) Figure 16 Price of lead acid batteries vs cumulative installations with learning rate based on AUD data points [data from (Battery-University, 2013; Eyer & Corey, 2010; Nagy; Schoenung, 2001, 2011; Sunlight, 2013)] Page 29 of 79

Figure 17 Price of lead acid batteries vs cumulative installations [data from (Battery-University, 2013; Eyer & Corey, 2010; Nagy; Schoenung, 2001, 2011; Sunlight, 2013)] Page 30 of 79

3.4.2 Forecasts In order to establish a starting point for forecasts of future battery prices, a market and literature survey was undertaken (See Table 3). Essentially three price points could be observed for the new energy storage batteries: ~$350/kWh for advanced lead acid and sodium sulphur batteries, ~$400/kWh for zinc bromine, and ~$600/kWh for lithium ion and more expensive estimates of advanced lead acid. As a result of this, starting points for three advanced battery scenarios were taken to be $350/kWh, $475/kWh, and $600/kWh. Straight lead acid batteries were observed to be selling for a large range of prices, $140 - $300/kWh; so, another scenario was modelled to reflect the lower cost of lead acid batteries and the smaller relative increase in cumulative installed capacity. Table 3 Survey of current battery prices Lead acid (Surrette Flooded Lead Acid Battery 6V 600Ah, C20) Type Source AUD2013/kW h100 percent DOD Lead acid (Trojan T-105 http://www.probatteries.com.au/trojan-batteries.html $143 Battery, Wet Flooded Deep Cycle, C20) Lead acid (Trojan Deep Cycle http://www.apolloenergy.com.au/products/6vbatteries/j305h-ac-6v $231 Battery 6V 375Ah) Lead acid (Energystore http://www.planetarypower.com.au/solar_batteries.ht $252 6PR670 6V 670 amp-hour m battery, C20) http://www.apolloenergy.com.au/products/6vbatteries/s-600 Adv lead acid (Schoenung, 2011) $347 Adv lead acid with carbon (Schoenung, 2011) $347 NaS (Schoenung, 2011) $368 Zinc bromine http://www.theaustralian.com.au/business/opinion/ene $400 rgy-storage-group-redflow-works-to-rechargebatteries/story-e6frg9if-1226430387068 Zinc bromine http://www.climatechange.gov.au/sites/climatechange/ $410 files/files/reducing-carbon/appendix8-csiro-energystorage.pdf Zinc bromine (Schoenung, 2011) $420 lithium ion (Schoenung, 2011) $630 Advanced lead-acid http://www.climatechange.gov.au/sites/climatechange/ $699 files/files/reducing-carbon/appendix8-csiro-energystorage.pdf Lithium ion phosphate Solar Australia Battery Storage (note: price includes power system, cabinet, wiring) $1,191 $296 Page 31 of 79

The current global installed energy storage capacity of batteries is ~556 MW including flywheels (EAC, 2012). Assuming the average rated discharge time to be 4 hours, battery installed capacity is ~2.2 GWh. This was rounded to 2.5 GWh and taken to be the current installed capacity. There is very large growth being forecast for the energy storage market given falling storage costs, rising electricity prices, rising costs of transmission and distribution, the difficulties of handling intermittent renewables, and Germany s recently implemented energy storage subsidy. These forecasts varied widely and are difficult to compare given the nature of storage systems: IEA-ETSAP and IRENA (2012) stated that the global energy storage market was expected to grow by 20 times between 2010 and 2020, which is equivalent to ~35 percent annual growth rate. Renewable_Energy_World.com (2012) published forecasts from Lux Research that predicted global demand for energy storage to reach ~185 GWh by 2017, with annual growth of 230 percent for the next 3 years before falling to 43 percent for the following 2 years, with a caution that the market may become supply constrained. Eyer and Corey (2010) of Sandia estimate the maximum 10yr market potential in the US for energy storage to be ~350 GW. Assuming an average of 4hrs, this equates to 1,400 GWh. Energy-Matters (2013) published an IMS Research prediction that the PV storage market would grow by 100 percent per annum for the next 5 years to $19b by 2017 as a result of Germany s energy storage subsidy. At an average price of $400/kWh, this would equate to 47.5 GWh of PV storage installations alone in 2017. Marchment-Hill (2012) advised the Clean Energy Council that the commercial market for energy storage in Australia would grow by 20 percent per annum to ~1 GW by 2020. Assuming 4 hrs discharge, this equates to 4 GWh. Navigant-Research (2013a) published forecasts that the installed capacity of advanced lead acid batteries to grow by ~80 percent per annum to 5 GW by 2020. Energy storage forecasts are often published without context; for example, often only MW are quoted with no reference to storage capacity, or published figures only refer to part of the market. This makes it difficult to compare forecasts from different sources. However, what is clear is that large growth is Page 32 of 79

forecast, and if the rapid PV uptake is any guide to go by, one would expect these forecast high growth rates to eventuate. In order to forecast future battery storage costs the following scenarios were assumed: 1. Low growth and learning with 30 percent annual growth for 2013 to 2016, and 20 percent for 2017 to 2020, and a learning rate of 10 percent. 2. Medium growth and learning with 40 percent annual growth for 2013 to 2016, and 30 percent for 2017 to 2020, and a learning rate of 15 percent. 3. High growth and learning with 150 percent annual growth for 2013 to 2016, and 40 percent for 2017 to 2020, and a learning rate of 20 percent. Note: a learning rate of 20 percent was chosen as it was deemed unrealistic to use 25 percent when combined with such extreme growth rates. 4. In addition, a scenario was included to reflect that status of lead acid batteries as a mature technology with annual growth rates of 15 percent, and a long term average learning rate of ~18 percent. The forecast range of future energy battery storage prices for all technologies is shown in Figure 18 with a price range of between $100 and $500/kWh by 2016, and $70 and $460/kWh by 2020. This provides a reasonable picture of the range of costs that could be expected for different battery technologies out to 2020. However, the range is larger than necessary for the purpose of comparing future costs of generation of a PV with battery system to centralised electricity prices because lead acid batteries are a mature technology with unsubsidised costs that are well established. There are two exceptions to this point: the first is that new technology may become a cheaper option in the future, as shown in Figure 18 where the high scenario curve intersects the lead acid curve around 2015; the second is that there are several key battery characteristics, depth of discharge and roundtrip efficiency, that may lead to a more advanced technology with higher capital costs having a lower life cycle cost. To explain the second point further, the life time of a battery is related to the number of cycles (discharging and charging events) and the depth of each discharging event. The greater the depth of discharge, the fewer cycles a battery can withstand. With regard to lead acid batteries the depth of Page 33 of 79

discharge allowable to achieve a long operating life is quite minimal: ~30 percent to achieve ~2,000 cycles (Schoenung, 2011), which is only about 5.5 years at one cycle per day. This effectively increases the amount of batteries required to achieve a longer life, or increases operating costs as the batteries need to be replaced more frequently, both of which increase life cycle costs and need to be optimised. In contrast, more advanced battery technologies can provide longer life with much greater depths of discharge: published figures suggest 65 percent 90 percent for 3,000 to 10,000 cycles (Battery-University, 2013; Climate_Commission, 2011; Schoenung, 2001, 2011). In addition, round trip efficiency needs to be considered; i.e., the amount of energy that is lost in the storing and conversion process. Lead acid batteries do perform quite well in this regard with published figures of ~85 percent (Battery-University, 2013; Climate_Commission, 2011; Schoenung, 2011), but a more advanced battery may have a better efficiency. Both of these factors need to be considered from a life cycle cost perspective in order to select the option that will deliver the lowest overall cost of generation. As a result of this rationalisation process, Figure 18 was redone (see Figure 19) considering an assumed depth of discharge and efficiency. Advanced batteries were given a 80 percent depth of discharge and 80 percent efficiency, while lead acid batteries were given 50 percent and 85 percent (values taken from Schoenung (2011)). From this analysis it was decided to disregard the low scenario as the lead acid battery could be selected instead, include the medium scenario to account for uncertainty in the price forecasts of lead acid batteries to the high side, and include the high scenario because the life cycle costs of advanced batteries under this scenario are likely to be lower than the lead acid option. Page 34 of 79

Figure 18 Forecast global battery prices over time Figure 19 Global battery net prices over time considering DOD and roundtrip efficiency Page 35 of 79

3.5 Balance of system costs Additional equipment and costs were included in the analysis to account for adding storage to a PV system. This included: a more expensive inverter capable of integrating storage, PV and a load; a combined maximum power point tracker (MPPT) and battery charger to optimise output from the PV system and control the charging and discharging; and an allowance to cover the Balance of System, which includes additional labour and miscellaneous materials such as wiring and connectors. The additional cost for the inverter and MPPT/charger was determined from comparing retail costs of such equipment and adding a value in $/kw to the initial forecast price of the PV systems. The relative cost reduction over time for these additional items was then assumed to be the same as that for the PV systems; i.e. effectively assuming technological learning to be the same. With regard to the additional labour and equipment, this was included to account for the additional work required in connecting storage into the PV system. As it was a small portion of the overall costs, it was assumed to be a flat rate in $/kwh of storage that stayed constant over time. It was deemed reasonable to use the experience curves already established for PV systems given that the type of equipment and skills required are essentially the same. Table 4 gives an example of how these additional costs were added to the PV and battery system costs to give a total installed system cost. Table 4 Example of forecast total system cost ranges Year PV System installed ($/kw) Allowance for off grid inverter ($/kw) 1 Allowance for MPPT/ Charge controller ($/kw) 1,2 Net Storage ($/kwh) Misc equip & labour ($/kwh) 2 Example system cost 5kW PV + 24kWh storage (AUD2013) 2013 2000-3000 200-600 300-600 370-690 70 23,000 39,170 2014 1840-2960 180-590 280-590 300-640 70 20,460 37,640 2015 1690-2920 170-580 250-580 230-590 70 17,670 36,210 2016 1550-2870 160-570 230-570 170-540 70 15,410 34,790 2017 1430-2830 140-570 210-570 150-510 70 14,240 33,730 2018 1310-2790 130-560 200-560 140-480 70 13,120 32,720 2019 1200-2750 120-550 180-550 120-450 70 12,100 31,750 2020 1110-2710 110-540 170-540 110-420 70 11,240 30,830 1. http://www.apolloenergy.com.au/products/sma-charge-controllers/sic40-mpt (On grid $500-700/kW, Off grid $900-1100/kW) 2. Schoenung (2011) 3. Schoenung (2001) Page 36 of 79

3.6 Homer Modelling The Homer Energy Modelling Software was developed by the National Renewable Energy Lab (NREL) and then commercialized through HOMER Energy. The NREL developed the software during the 1992 Village Power Program aimed at helping developing countries incorporate renewable power into their rural electrification program. The original specialized software ran on a UNIX workstation, but was converted to a Windows application in C++ in 1997 for broader community use. It is a tool for designing, analysing and optimizing hybrid power systems, which contain a mix of conventional generators, solar photovoltaics, batteries, and other renewable and distributed generation technologies. This section explains how the system was modelled in Homer and presents a sensitivity analysis on the main assumptions to provide indication of the key parameters and broader applicability of the results. 3.6.1 Model creation The system was designed in Homer as is shown in Figure 20. The system was configured in parallel arrangement with a DC bus and a converter to AC bus. This is a common configuration that allows the PV modules to directly supply the load when generating and reduce storage conversion efficiency losses, and allows both the PV modules and batteries to simultaneously meet the load at periods of high demand. 20 different models were created to account for the different scenarios: A low growth, low learning scenario with lead acid batteries that assumes high capital price assumptions and lower learning rates. The same scenario as above but with advanced batteries. A high growth, high learning scenario with lead acid batteries that assumes low capital price assumptions and higher learning rates The same scenario as above but with advanced batteries. All four scenarios were then repeated for a 50% and a 75% solar penetration scenario assuming both a 30c/kWh and 40c/kWh alternate energy price, giving 20 models in total. Page 37 of 79

The detailed inputs for each model including the performance assumptions used for the components are shown in Appendix A Homer Model Inputs. Within each model, multipliers were set up to simulate the reducing costs of the components over time. Different multipliers were applied to each component group to reflect the different learning rates established for the modules and the batteries. Each year was set up as a sensitivity case and by linking each component group multiplier with each other the model automatically updated all component costs for each case. Once the model was established, many scenarios were run to optimise the number of component different sizes that Homer could select to keep the number simulations to a sensible size. The details of the sizes considered for each are shown in Appendix A Homer Model Inputs. The control system was set to load follow with 0% annual capacity shortage and 100% solar power output. Homer then calculated the levelised cost of energy for every possible different sized system within the constraints set and produced the optimum system configuration. To give an indication of the system sizes that were used in the analysis, Homer calculated the optimum sized system to be as follows: For the 100% penetration case: ~11 kw of PV panels, 60-80 kwh of storage, and a 4 kw converter. For the 75% penetration case: ~6 kw of PV panels, 20-40 kwh of storage, and a 2 kw converter. For the 50% penetration case: ~4 kw of PV panels, 10-30 kwh of storage, and a 1 kw converter. The levelised cost for the optimum system for each scenario over time was then extracted from Homer and graphed against forecasted future electricity prices. Page 38 of 79

Figure 20 System design in Homer Model 3.6.2 Component cost assumptions The component cost assumptions developed in Sections 3.3, 3.4, and 3.5 for each different scenario were input into the model. The exact inputs are shown in Appendix A Homer Model Inputs. 3.6.3 Financial assumptions A real discount rate of 10 percent was assumed. This is high relative to common analysis of power generation projects, but thought to more accurately represent the faster payback required to entice the average residential/small business consumer. A project life 15yrs was assumed. This was thought to be a suitable time given that under the Smallscale Renewable Energy Scheme (SRES) a eligible PV system can be granted small-scale technology certificates for 15 years in advance (CER 2013). In addition, PV panels are being sold today with 80% performance warranties for 25 years (see Appendix B PV Module Data Sheet), and extended inverter warranties are available for up to 20 years (Fronius 2013). Maintenance costs were ignored, apart from replacement costs. This was deemed appropriate as many small consumers would not typically have any maintenance costs (apart from their own labour), and in many cases would simply not do any. 3.6.4 Solar radiation and orientation assumptions Ambient temperature and solar radiation data was based on Brisbane, Queensland and is shown in Appendix A Homer Model Inputs. A fixed north facing PV system with latitude tilt was assumed. Page 39 of 79

3.6.5 PV module performance assumptions The performance characteristics of the PV module for modelling purposes were taken from the data sheet of a Sun-Earth 250W panel (See Appendix B PV Module Data Sheet). The inputs into Homer are shown in Figure 21. Figure 21 Solar PV module performance inputs into Homer model Page 40 of 79

3.6.6 Battery performance assumptions The performance characteristics for the batteries used in the model were those already existing in the Homer Model. Zinc Bromine battery was assumed to be the advanced battery technology, and the Trojan T-105 battery was assumed to be the lead acid battery. The default values for these batteries are shown in Figure 22 and Figure 23. Figure 22 Homer default values for Zinc Bromine Batteries Figure 23 Homer default values for Trojan T-105 lead acid batteries Page 41 of 79

3.6.7 Load Profile assumptions The load profile used was a scaled version of a sample load for a remote load provided by Homer Energy with an increased daily variance. The key parameters of the load are 7,300 kwh annual consumption, 4 kw peak load, random variability day to day of 47 percent, and hour to hour variability of 15.8 percent. Some demand management is assumed as this would be normal for a grid isolated consumer. The annual load profile is shown in Figure 24. The annual consumption and peak load figures have been verified comparing data from Simshauser and Laochumnanvanit (2012), who published energy use data from 3,000 randomly selected homes in Sydney, New South Wales. Sydney could be considered a conservative comparison for the assumed load profile assumed in Brisbane. This is because the weather variations in Sydney are more extreme and tend to give higher summer temperatures and colder winter temperatures leading to higher peak loads and energy consumption. Figure 25 shows this data as an average daily load profile and the average load profile on maximum peak demand day, which was a 40 C summers day. Figure 26 shows that the load profile for a hot day in the Homer Model is quite similar to Figure 25. The lower peak demand used in the model is assumed to be a benefit of demand management; the capability of which is assumed to be included in the solar +PV system. In addition, Simshauser and Laochumnanvanit (2012) publish that the average annual demand for these house was 6,700 kwh, compared to a regional average of 7,500 kwh. The load factor for the assumed load is ~20%, which is more conservative than what Simshauser and Laochumnanvanit (2012) state is the typical load factor of a high peak household of 30 to 40%. This data provides evidence that the assumed load profile is both reasonable and representative of Australia residential home energy consumption trends. Further evidence of the credibility of the load profile is given by comparing the load duration curves shown in Figure 27 and Figure 28. Figure 27 is a load duration curve for a new residential housing development in South Australia (Saman & Halawa 2009), and Figure 28 is the load duration curve from the Homer model. The random variability day to day assumption of 47 percent was increased from the default value in the Homer program of 19.7%. It was thought that this better represented the day to day variations that Page 42 of 79

can occur in a household due to consumption patterns changing from to week days to weekends and from large day to day fluctuations in weather conditions. The time step to time step variability was left as the default value. Figure 24 Assumed load profile for analysis (Extract from Homer Energy Model) Page 43 of 79

Figure 25 NSW Household demand annual average vs critical event day (Simshauser and Laochumnanvanit 2012) Figure 26 Daily load profile example for assumed load (Extract from Homer Energy Model) Page 44 of 79

Figure 27 Load duration curve for load profile used (Extract from Homer Energy Model) Figure 28 Load duration curve for a new residential housing development in South Australia (Saman & Halawa 2009) Page 45 of 79

3.6.8 Sensitivity analysis A sensitivity analysis was carried out to on the key assumptions to provide indication of the dominant parameters and broader applicability of the results. A sensitivity analysis was carried out on both the advanced battery scenarios: the high learning, low cost scenario, and the low learning, high cost scenario, as these represented the extreme cases. The key assumptions used were: Capital Costs +/-50% Discount Rate 5% - 15% Increased Load Variability 10% - 105%, and Peak Load 2.5 kw 6 kw Solar radiation 4.44 6.66 kwh/m 2 /day. The lower value is typical of Southern Victoria and New South Wales and can be considered a poor solar resource in Australia. The higher value is typical of Western Queensland and Northern Western Australia and can be considered an excellent solar resource in Australia. Brisbane has an average solar resource of 4.81 kwh/m 2 /day (Bureau of Meteorology 2009) PV Orientation East, West and North were considered. The results are presented in Figure 29 and Figure 30. The most dominant factor was capital costs. This validates the large effort spent in determining accurate capital costs. It also supports the notion that the results are best viewed as a range because forecasting future technology capital costs is inherently difficult. The next dominant factor was discount rate, which varied the LCOE by ~+/-30%. This is important because it suggests that the consumer can influence the time to when stand alone PV is competitive with the grid just by changing their acceptable return perceptions. The load profile or solar parameters did not vary the results significantly. Even the extreme load case of 100% day to day variability and a 50% higher peak load of 6 kw only increased the LCOE by ~20%. Large changes in orientation and in solar radiation had less than 20% influence on the LCOE. The results of the sensitivity analysis support focusing attention on capital cost accuracy and changes over time and indicated that the results are applicable to many areas of Australia. Page 46 of 79

60% Sensitivity Analysis for Advanced Battery High Learning, Low Cost Scenario - % Change in LCOE Capital Costs (+/-50%) 40% Discount Rate (5%-15%) 20% PV Orientation (East to West) 0% -20% -40% Solar Radiation (4.44 kwh/day - Equivalent to Southern Victoria to 6.66 kwh/day - Equivalent to Western Queensland and Northern Western Australia) Peak Load (2.5 kw to 6 kw) -60% Figure 29 Sensitivity Analysis for Advanced Battery High Learning, Low Cost Scenario - % Change in LCOE Sensitivity Analysis for Advanced Battery Low Learning, High Cost Scenario - % Change in LCOE 60% Capital Costs (+/-50%) 40% Discount Rate (5%-15%) 20% PV Orientation (East to West) 0% -20% -40% Solar Radiation (4.44 kwh/day - Equivalent to Southern Victoria to 6.66 kwh/day - Equivalent to Western Queensland and Northern Western Australia) Peak Load (2.5 kw to 6 kw) -60% Figure 30 Sensitivity Analysis for Advanced Battery Low Learning, High Cost Scenario - % Change in LCOE Page 47 of 79

4 Analysis 4.1 Centralised grid vs standalone PV with battery storage In order to estimate when it might be cost effective for consumers to disconnect from the grid for their own standalone PV with storage system the forecast cost of generation was compared with the estimated electricity prices over time. Firstly, the results are present graphically and explained textually; followed by a broader discussion about the implications of the results. 4.1.1 Results Figure 31 shows the results for the low electricity price states: The high growth, high learning with advanced batteries scenario breaks even between 2017 and 2020. Depending on future electricity price rises, grid disconnection under this scenario could become viable sometime between 2017 and 2020. The high growth, high learning with lead acid batteries option would appear to break even slightly beyond 2020 with a high electricity price, or if electricity price rises were higher than that forecast. Both the low growth, low learning scenarios appear to remain uncompetitive for an extended period into the future. These results suggest that the breakeven point for a standalone DG system in states with lower electricity prices will most likely not occur in the short to medium term (i.e. <10 years). However, if energy storage learning rates do follow a similar trend to that of PV over the last ~5 years there is a possibility of DG systems reaching breakeven point within this time. Figure 32 shows the results for the high electricity price states: Both high growth, high learning scenarios are forecast to become cost competitive before 2020, with the advanced battery scenario competitive sometime between 2015 and 2017, and the lead acid battery option competitive between 2018 and 2020. Again, both the low growth, low learning scenarios remain uncompetitive for an extended period into the future. These results reinforce the point that standalone DG systems are more likely to remain noncompetitive in the short term (<5 years). However, given the high growth, high learning scenario with Page 48 of 79

proven lead acid batteries breaks even around 2017, it is suggested that it is more likely than not that a standalone DG system will be competitive in the medium term. 4.1.2 Discussion It is important to note that the analysis treats the cost of PV systems and electricity prices as independent variables; therefore, it does not account for any interrelationships between the variables that may affect the actual outcome. This is thought to be a reasonable assumption given that the overall installed capacity of DG is likely to be relatively small leading up to and shortly after breakeven point. Consequently, noticeable negative consequences for incumbent utilities may lag the breakeven point by some years meaning that their reactive actions during this time are likely to be minor. Evidence from Germany supports this where distributed PV now provides ~5.3 percent of total electricity generation, yet only two senior representatives out of 18 German utilities perceive distributed PV to be a threat to their business model (Richter, 2012). This creeping nature of penetration may also impair incumbent utilities from recognising business opportunities that present themselves. Richter (2012) advised that In the first half of 2012, Germany had produced 25 percent of its electricity from renewable sources., yet utilities only own 13.5 percent of total renewable generation capacity. This section explains some of the interrelationships that are expected to impact the issue in practice. Significant market changes do not typically occur at steady, long term average rates. Changes may be subtle for extended periods of time, and then occur very rapidly when market and political conditions align to favour a particular change. This characteristic has been clearly observed in the PV industry with above trend learning rates of ~28 percent over the last 5 years (Section 3.3), and in electricity prices which rose at around CPI from 1995 to 2007 before rising between 40 and 120 percent over the last 5 years (AER, 2012). Independent variable analysis also unrealistically implies that neither party reacts to the competitive threat brought by the other and/or that there is no market intervention. As an example, it implies that incumbent utilities do nothing and just raise electricity prices. However, while evidence from Germany suggests utilities will do little until well after breakeven point when installed capacity becomes significant, they may start to compete by compressing margins, cutting costs, and increasing efficiency. In addition, one might anticipate some government intervention which could delay or bring forward the uptake of distributed generation as discussed in section 5.3. Conversely, if government Page 49 of 79

incentives for energy storage were introduced, such as seen in the PV industry, this could drive PV with storage to be cost competitive sooner. In addition to these factors, there is also the unpredictable behaviour of consumers. In the electricity sector, consumers are increasingly participating in the market by installing their own PV systems, and increasing energy efficiency and energy management (Parkinson, 2013a). This group of prosumers may choose to disconnect from the grid in advance of true grid parity because the concept of generating their own renewable energy is appealing, and locking in electricity costs for 10-20 years creates certainty against future rises in grid prices. Conversely, even in the case that DG systems are competitive many consumers may choose to remain with grid electricity because they cannot, or choose not to, pay for such a system; may not be able to recoup their investment if moving house in the short term; or not want the inconvenience of maintenance or having to employ energy management techniques. Simply put, given that electricity is still a low contributor to the overall household budget (Nelson & Simshauser, 2012) many consumers may be willing to pay extra for the convenience of grid electricity. Although, these hurdles may be overcome by innovative finance options offered by companies seeking to profit from the competitive advantage of DG. As discussed, there is a complex range of interrelated variables that would affect the actual outcome, but the purpose of the analysis is to use long term trends to identify whether breakeven will occur in the short term (<5 years), medium term (5-10 years), or long term (>10 years). These results suggest that breakeven, i.e. grid parity of DG systems, will occur in the short to medium term in high price states, and in the medium to long term in low price states. Page 50 of 79

Figure 31 Forecast PV with storage costs vs predicted electricity price (low price states) Figure 32 Forecast PV with storage costs vs predicted electricity price (high price states) Page 51 of 79

4.2 Grid connected PV with battery storage systems at high penetration levels An analysis was carried out to see the effect of reducing PV penetration on the results. 4.2.1 Results In order to carry out the analysis, it was assumed that the consumer was connected to the grid, and could import electricity whenever desired, but could not export any. Two scenarios were modelled using the Homer package: one with an electricity price of $0.3/kWh, and the other with an electricity price of $0.4/kWh. Note that because export was not allowed the inclusion of the grid does not necessarily mean that the scenario reflects only a grid connected option. The grid represents any additional dispatchable power source with a cost of generation of $0.3/kWh, or $0.4/kWh, and could just as well be a fuel cell or micro generator. The minimum renewable energy penetration level was set at 50 percent, and 75 percent. Figure 33 shows that at 75 percent penetration with an external alternate source of electricity at $0.3/kWh both high growth, high learning cases become cost competitive in the near future, with the advanced battery option competitive by 2014, and the lead acid battery option competitive sometime between 2016 and 2018. Interestingly, even the low growth, low learning scenarios are close to competitiveness with a high electricity price by 2020. Figure 34 shows the same comparison but with an alternate source of electricity cost of $0.4/kWh. Effectively both high growth, high learning (high) scenarios are competitive now. The low growth, low learning scenarios are competitive as early as 2018 with a high electricity price, are competitive with a medium electricity price case by 2020, and by extrapolation likely to be competitive with a low electricity price scenario by 2025. The same analysis was carried out for a 50 percent penetration scenario for both $0.3/kWh and $0.4/kWh alternate source cases, shown in Figure 35 and Figure 36. For the $0.3/kWh case the high growth, high learning advanced battery option is basically competitive now. The high growth, high learning lead acid option becomes competitive somewhere between 2015 and 2017. Both low growth, low learning options are competitive by 2020, except with a low electricity price. For the $0.4/kWh case both high growth, high learning scenarios are competitive now, and both low growth, low learning scenarios are competitive by 2020 with all assumed electricity price scenarios. Page 52 of 79

The interesting conclusion from this analysis is that, in the low electricity price states, it is most likely that a PV system will be able to competitively displace between 50 and 75 percent of grid electricity by 2020, and under many scenarios as early as 2015. In the high electricity price states it is highly likely that between 50 and 75 percent of electricity consumption could be supplied competitively by a PV system by 2015, and almost certainly by 2020. In summary, in the very near future, a large percentage of Australian consumers could cost effectively meet the majority of their electricity needs with a DG system. 4.2.2 Discussion While the main research question is about consumers disconnecting from the grid, large reductions in grid consumption could also have a significant impact on the current electricity market. This is especially the case for generators whose income is mainly reliant on the amount of electricity sold and the wholesale price realised. Impacts have been observed in the German electricity market where PV is reducing demand for conventional generation, and reducing peak wholesale electricity prices as demonstrated in Figure 37. As evidence, in Germany, the renewables contribution to total electricity production grew from 11 to 17 percent from 2005 to 2010 (Sioshansi, 2013), which has negatively impacted on incumbent generators margins as shown in Figure 38. Note that the rise in margins in 2011 was due to the forced shutdown of eight nuclear plants (Sioshansi, 2013). The findings of the high penetration analysis give weight to the argument that DG technologies with some storage may be cost competitive now in the right application. The evidence for this is as follows: grid connected PV systems without storage can already generate cheaper than the retail electricity price, a low cost grid connected PV system with proven lead acid storage is calculated to achieve ~50 percent penetration at ~$0.42/kWh, and summer peak prices in South Australia are already at ~$0.42/kWh. The conclusion is that it is likely that some Australian consumers could cost effectively generate ~50 percent of their own power from a PV with storage system now. Severance (2011) supports the notion and stated; With major players now pushing distributed power generation, the days of a captive customer base for central electric utilities are over. It should also be reinforced that the analysis did not allow export to the grid to imply that the alternate source of electricity could be another DG technology such as a fuel cell or micro turbine; providing the technology could generate for equal to or less than the grid price. Given technological advancements in fuel cells and micro turbines, and the efficiency advantage they offer in a combined heat and power Page 53 of 79

arrangement, it is easy to imagine standalone DG systems incorporating solar PV, energy storage, and some form of dispatchable combined heat and power technology being competitive with the grid in the not too distant future. Severance (2011) believes this is the case and stated; Electric customers can now walk away from their central utility not only through efficiency, but also by generating their own power. Combined heat and power has long offered large customers a costeffective distributed power solution whose use is growing rapidly, and now on-site power is entering a new era. The results of the high penetration analysis suggest that it is likely that consumers will be able to cost effectively offset large portions of their electricity usage with DG systems in the short term (<5 years). Subsequently, significant changes to the electricity market may be just around the corner. Figure 33 Forecast PV with storage costs at 75 percent min RE penetration with external source at 30c/kWh Page 54 of 79

Figure 34 Forecast PV with storage costs at 75 percent min RE penetration with external source at 40c/kWh Figure 35 Forecast PV with storage costs at 50 percent min RE penetration with external source at 30c/kWh Page 55 of 79

Figure 36 Forecast PV with storage costs at 50 percent min RE penetration with external source at 40c/kWh Figure 37 Impact of PV on the spot market price in Germany (Sioshansi, 2013) Page 56 of 79

Figure 38 Decreasing gross margins for German fossil fuel electricity plants from 2004 to 2012 (Sioshansi, 2013) Page 57 of 79

5 Discussion on impacts on electricity markets This section discusses what the effects and issues of increasing distributed generation penetration on the current electricity market in Australia may be. 5.1 The concept of disruptive technologies Gerald (2001), Richter (2012), (Rogers, 2012), Kind (2013), and Newbury (2013) all discuss distributed generation as a disruptive technology that is set to change the game in the electricity market. A disruptive technology can be defined as one that replaces existing technologies, or changes the market structure, as opposed to a sustaining technology that improves the performance of the existing players and market (Richter, 2012). In the words of Richter (2012), disruptive innovations often destroy the value of existing competencies, and therefore incumbent players must innovate to stay competitive. Other notable industries have faced disruptive innovations in the past: the photograph industry, computers, telecommunications, and the postal service. Some incumbents have evolved and survived, while others have perished. Kodak was considered the incumbent in the film based photos market and eventually went bankrupt in 2012 due to the disruptive nature of digital photography (Kind, 2013). IBM nearly went to the wall due to the development of PC technology, but eventually evolved from a manufacturer of main frame computers into a technology services provider (Newbury, 2013). Postal services all around the world, many that did operate as monopolies, are under threat due to the combined effect of the various methods of digital communication (Kind, 2013). The technological innovation of wireless telecommunications has effectively eliminated the need for fixed phone lines, and made the previous regulated natural monopoly business model obsolete (Kind, 2013). Newbury (2013) suggests that DG technologies are likely to trigger the creative destruction of existing natural monopolies and render incumbent business models unsustainable. The threat for utilities is enhanced by the fact that energy efficiency, demand side management and changing consumer behaviour are all combining to reduce grid electricity demand (Kind, 2013). There is little doubt in the minds of Gerald (2001), Richter (2012), Rogers (2012), Kind (2013), and Newbury (2013) that DG is going to significantly change the electricity market. Their main uncertainties are around when it is likely to occur, how electricity markets will change, and whether incumbent utilities can survive the incumbent s curse. Page 58 of 79

The incumbents curse, or innovator s dilemma, is a phenomenon that refers to the difficulties faced by incumbent players in responding to disruptive technologies (Newbury, 2013). It is widely acknowledged in the literature that incumbents have difficulties in responding to disruptive threats (Newbury, 2013), with some of the main reasons being: Incumbents often overlook disruptive technologies because the technologies typically do not initially satisfy the demands of the market and have low profitability compared to the status quo. As a result incumbent firms are often too late in making investments in disruptive technologies and are left behind (Newbury, 2013). It is difficult for a firm to simultaneously exploit their existing capabilities while exploring for new competencies because of the inherently different organisational cultures required to excel at either one (Newbury, 2013; Richter, 2012). As a result, many incumbent firms are heavily focused on incremental or sustaining innovation that seeks to improve their existing products and/or business model, at the expense of exploring for new innovations (Richter, 2012). Related to the first two points is the typical low risk tolerance of established incumbents which directs most research and development funding towards incremental innovation rather than game changing technologies that come with higher investment risk (Newbury, 2013). With regard to natural monopolies, a lack of competition and complacency about the sustainability of their business model limits their ability to create, or take advantage of, disruptive innovation (Newbury, 2013). They are also likely to lack the competencies, and find it difficult, to adapt from a monopolistic position to a competitive market (Newbury, 2013). Finally, government owned utilities may have further difficulties given the high levels of bureaucracy and the fact that they are often directed to serve short term political interests rather than the long term sustainability of their business model (Newbury, 2013). Page 59 of 79

5.2 The impact of disruptive innovation on incumbent utilities In order to provide insight into how incumbent utilities were placed to handle the disruptive threat posed by DG and the incumbents curse, Newbury (2013) interviewed 18 of the 22 distribution network utilities in Australia and the UK. Newbury (2013) reported that most underestimated the threat of DG technologies; it was thought that the intermittency of renewable energy meant that consumers will always need to be grid connected. DG was also considered as an isolated risk from other technologies such as energy efficiency (Newbury, 2013). These utilities are failing to see the whole picture: energy storage technology is advancing at a rapid rate, so are dispatchable DG technologies such as fuel cells, and these combined with energy efficiency and energy management technologies pose a very real threat to the centralised generation model. Newbury (2013) commented that: The findings suggest electricity distribution network utilities will face a range of significant challenges and survival is by no means certain. Similarly, Richter (2012) interviewed 18 German utilities on the same topic and reported that the overwhelming majority of German utilities do not perceive DG to be a threat to their existing business model, nor see any market opportunities. Richter (2012) went on to say that German utilities have lost 97 percent of the distributed PV market, and despite the numerous examples of the impact of disruptive innovations in other industries, incumbent utilities are failing to adapt to the changing electricity market. DRET (2012) suggests that the main impact of DG on incumbent generators, networks, and retailers is falling demand for grid electricity which will correspond to falling revenues. At first, insignificant consequences for utilities are expected given the penetration levels of DG will be relatively minor as discussed in section 4.2.2. As time progresses and DG penetration increases, one would expect utilities to start competing by compressing margins, cutting costs, and increasing efficiency with a minor business impact. As DG costs decrease further, some consumers may choose to disconnect from the grid. This combined with further DG penetration will leave utilities attempting to recover revenues from a dwindling demand base causing them to raise unit electricity prices. Subsequently, more consumers will turn to DG technologies, and the death spiral scenario of falling demand and rising electricity prices has become a reality (see Figure 39). This then presents the risk of stranded assets where utilities cannot recover their investment because there is no longer sufficient demand for the service Page 60 of 79

that the asset provides (Parkinson, 2013b). Given that most electricity utility investments are made on a 20 plus year timeframe, this presents significant financial risks to the utility and utility investor (Kind, 2013). Figure 39 The death spiral concept (Kind, 2013) 5.3 Market intervention Government intervention is likely; this is evident by the significant amount of recent government reviews and reports on the topic. Passey and Watt (2013) highlighted the Australian Energy Market Commission s (AEMC) Power of Choice report that reviewed the market and regulatory arrangements needed to facilitate demand side participation. Watt, Passey, and Morris (2013) were also partly funded by a government agency, the Australian Renewable Energy Agency, to carry out a study: A Distributed Energy Market: Consumer & Utility Interest, and the Regulatory Requirements. The Productivity Commission was tasked by the Australian Government to examine the possible benchmarking of regulated networks, among other network issues (DRET, 2012). In addition, the Standing Council on Energy and Resources (SCER), on behalf of the Council of Australian Governments (COAG), reported on recommended reforms to national electricity laws, including removing retail price regulation, and a framework for efficient demand response (DRET, 2012). DRET (2012) stated that: Australian governments must collectively undertake further market, regulatory and institutional reforms to ensure the efficient supply of energy and responsiveness of demand. Greater competition will stimulate business innovation to offer consumers better services, including a suite of information and smart tools to help them control their energy use and keep costs down. Page 61 of 79

There are a number of reforms that are thought likely to occur given that they were observed as common recommendations in government and regulatory reports on the topic, including AEMC (2012b), DRET (2012), and the Senate Select Committee on Electricity Prices (2013). However, it should be noted that the federal government changed in September 2013 and some of the previous works mentioned may be now superseded, in particular the Energy White Paper by DRET (2012) as this department no longer exists. The common recommendations observed were: 1. improve network planning, and ensure that it takes into account DG and demand management; 2. enable greater consumer participation in the market, including demand side management; 3. enable the efficient uptake of energy efficiency, energy management, and DG technologies; and 4. remove price regulation, and implement cost reflective pricing, in particular for distribution networks. Some other government and industry bodies are lobbying for changes that would essentially serve to protect the existing business model: higher fixed charges, low feed-in tariffs, network limitations on distributed generation, and higher fixed charges for owners of PV systems (Watt et al., 2013). It goes without saying that improved network planning that considers DG technologies, enabling greater consumer participation, and enabling the uptake of DG technologies will act to hasten the implementation of such technologies, and thus reduce costs quicker. Conversely, network limitations and higher fixed charges for PV owners would act to slow the uptake, and therefore delay the point in time at which such technologies become competitive. The impacts of price regulation and cost reflective pricing are not immediately obvious, and thus are discussed in more detail. Price regulation still exists in all states, except Victoria, and is considered to be a hindrance to efficient market outcomes including the uptake of DG technologies (Watt et al., 2013). DRET (2012) stated: The continued regulation of retail electricity prices in most states and territories is a continuing barrier to competition, innovation and investment. Simshauser and Laochumnanvanit (2012) discussed that price regulation led to the marginal cost of supplying a customer in NSW being more than the price cap in 2008, and ultimately the collapse of two retailers: EnergyOne and Jackgreen. While there are many advocates for deregulation of pricing, electricity pricing remains a political issue in many states Page 62 of 79

and the timing to deregulation is uncertain. The higher price in Victoria compared to the other comparable large NEM states, Queensland and New South Wales, suggests that removal of regulation would result in higher prices, and subsequently increase the uptake of distributed generation technologies. Structuring electricity tariffs to be more cost reflective of electricity supply is considered to be essential in achieving efficient market outcomes, but changes will affect the competitiveness of DG, especially when used in combination with grid supply. When considering a standalone DG system the competitiveness is not really affected as the comparison is based on annual costs to supply the required electricity.watt et al. (2013) summarise tariff changes commonly recommended by government and utilities in recent literature; these and the associated impacts to the competitiveness of DG are discussed below: Time of use tariffs that charge customers more at peak periods of the day and/or higher demand tariffs that penalise consumers for their contribution to peak demand encourage consumers to use less energy at these times, and subsequently reduce peak demand. Time of use tariffs are likely to incentivise the uptake of DG technologies because they give a higher competing variable tariff for parts of the day. In the case of grid connected PV systems, it s likely to be economic for consumers to install small amounts of storage with a simple energy management system that shifts cheaper PV produced power to times of peak prices. This will accelerate the uptake of storage which ultimately leads to faster cost reductions. Higher fixed charges are supposedly more cost reflective given that networks contribute significantly to the total cost, but their contribution is mainly fixed because of their high capital and low operating cost characteristics. However, it is argued that high fixed costs are not truly cost reflective in the long run; according to economic theory all costs are variable in the long run (About.com.economics, 2013). Therefore, high fixed costs are only cost reflective after the infrastructure has been built, and they do not provide accurate investment signals for comparing new network capacity with DG technologies. Higher fixed tariffs and lower variable tariffs are likely to slow the uptake of DG technologies Page 63 of 79

because grid connected DG competes with the variable tariff of grid electricity. The additional consequence of low variable tariffs is that the economic case for energy efficiency and energy management technologies, and energy saving behaviour, is negatively affected. Perhaps unintentionally, this could lead to a slower transition to a clean and low energy intensity economy, and missed opportunities in industry development for such technologies. Watt et al. (2013) suggest that higher fixed tariffs only serve to protect the revenues of incumbent utilities and are in conflict with National Electricity Rules. In summary, government intervention is likely to impact the uptake of DG technologies, and therefore, influencing the time at which they become competitive. In addition, lobbying by industry and some government bodies, in attempts to influence regulation and policy in favour of the existing business model, may serve to delay the uptake of DG technologies. This discussion gives an insight into the crowded and complex nature of energy policy, which often changes significantly between white/inquiry papers and enactment. In addition, the new Federal Government is likely to revise key energy policy documents such as the Energy White Paper 2013 by DRET (2012). Consequently, while intervention is likely, what intervention, and its likely impact on the uptake of DG technologies, is difficult to say with confidence at this point in time. Page 64 of 79

6 Conclusions The analysis revealed that solar PV with storage could be competitive with the grid in supplying 50 to 75 percent of a small consumer s electricity demand within the short term (<5 years), and on a standalone basis in the medium term (<10 years). In support of this, Newbury (2013) states that most estimates suggest grid parity of DG technologies will occur within 3 to 10 years. In comparison, investments for most incumbent generation and networks assets are made with a 20 to 50 year operational life in mind. When considering disconnection from the grid, i.e., 100 percent supply from PV with storage, the analysis revealed for the low price states, i.e., average prices of ~$0.30/kWh, that only the high growth, high learning with advanced batteries scenario becomes cost competitive prior to 2020. It also indicated that the high growth, high learning with lead acid batteries scenario would become cost competitive slightly beyond 2020, or if electricity price rises were higher than estimated, and that both low growth, low learning scenarios remain uncompetitive for the foreseeable future. For the high price states, i.e., average prices of ~$0.40/kWh, the analysis revealed that both high growth, high learning scenarios are forecast to become cost competitive before 2020, and as early as 2015; and both low growth, low learning scenarios remain uncompetitive for the foreseeable future. When considering the high penetration cases, i.e. between 50 and 75 percent of electricity supplied by PV with storage, the analysis revealed, for the low electricity price states, that it is likely that DG will be able to cost effectively displace between 50 and 75 percent of a consumers demand by 2020, and under many scenarios as early as 2015. For the high electricity price states, the analysis revealed that it is highly likely that between 50 and 75 percent of a consumers demand could be competitively supplied by a DG system by 2015, and almost certainly by 2020. These results are interesting because they suggest that a PV with storage system could be competitive with grid electricity in the near future, in some scenarios as early as 2015. When the potential of other distributed generation technologies, especially combined heat and power technologies, energy efficiency and demand side management are considered as well, change is likely sooner rather than later. The main impact of increasing DG penetration on incumbent utilities is falling demand for grid electricity, which equates to lower revenues. As a result, network service providers will need to raise Page 65 of 79

per unit transmission and distribution charges in order to recover the revenue required to meet their regulated return on asset base. This will drive electricity prices higher; which in turn will likely reduce consumers demand for grid electricity and increase DG penetration further. Because of falling demand, generators face lower wholesale prices, compressed margins, and the risk of stranded assets. Research into likely impacts on incumbent utilities and current electricity markets revealed that disruptive technologies, such as DG, typically cause significant market changes and create significant risks for incumbents, especially those in monopoly situations. Also that incumbent utilities are not well placed or prepared to handle competition from DG, and are slow in realising the threat and/or any market opportunities. Finally, government intervention is likely to impact the uptake of DG technologies, and therefore influence the time to which they become competitive at high penetration levels or on a standalone basis. However, given the crowded energy policy space and the recent change of Federal Government what intervention is likely, and its likely impact, is difficult to say with confidence at this point in time. Page 66 of 79

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8 Appendix A Homer Model Inputs Scenario 1A Low Learning, High Costs with Advanced Batteries Model Inputs Page 71 of 79

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Scenario 1B High Learning, Low Costs with Advanced Batteries Model Inputs (Changes to first model only) HOMER Input Summary File name: 1B PEC624 Adv Battery High Learning, Low Costs r1.hmr File version: 2.81 PV Size (kw) Capital ($) Replacement ($) O&M ($/yr) 1.000 2,000 1,000 0 Sizes to consider: Lifetime: 11.0, 11.5, 12.0, 12.5, 13.0 kw 25 yr Derating factor: 80% Tracking system: Slope: Azimuth: No Tracking 25 deg 180 deg Ground reflectance: 20% Battery: ZincBromine 1kWh Quantity Capital ($) Replacement ($) O&M ($/yr) 1 260 260 0.00 Quantities to consider: 20, 40, 60, 80, 100 Voltage: Nominal capacity: Lifetime throughput: 100 V 10 Ah 1,000 kwh Converter Size (kw) Capital ($) Replacement ($) O&M ($/yr) 1.000 500 500 0 Sizes to consider: 3, 4, 5, 6 kw Page 74 of 79

Scenario 1C Low Learning, High Costs with Lead Acid Batteries Model Inputs (Changes to first model only) HOMER Input Summary File name: 1C PEC624 Lead Acid Low Learning, High Costs r1.hmr File version: 2.81 Author: PV Size (kw) Capital ($) Replacement ($) O&M ($/yr) 1.000 3,000 1,500 0 Sizes to consider: 11.0, 11.5, 12.0, 12.5, 13.0 kw Battery: Trojan T-105 Quantity Capital ($) Replacement ($) O&M ($/yr) 1 210 210 0.00 Quantities to consider: 6, 7, 8, 9 Voltage: Nominal capacity: Lifetime throughput: 6 V 225 Ah 845 kwh Converter Size (kw) Capital ($) Replacement ($) O&M ($/yr) 1.000 1,200 1,200 0 Sizes to consider: 3, 4, 5, 6 kw Scenario 1D High Learning, Low Costs with Lead Acid Batteries Model Inputs (Changes to first model only) HOMER Input Summary File name: 1D PEC624 Lead Acid High Learning, Low Costs r1.hmr File version: 2.81 PV Page 75 of 79

Size (kw) Capital ($) Replacement ($) O&M ($/yr) 1.000 2,000 1,000 0 Sizes to consider: 11.0, 11.5, 12.0, 12.5, 13.0 kw Battery: Trojan T-105 Quantity Capital ($) Replacement ($) O&M ($/yr) 1 210 210 0.00 Quantities to consider: 6, 7, 8, 9 Voltage: Nominal capacity: Lifetime throughput: 6 V 225 Ah 845 kwh Converter Size (kw) Capital ($) Replacement ($) O&M ($/yr) 1.000 500 500 0 Sizes to consider: 3, 4, 5, 6 kw Page 76 of 79

Scenarios 2A to 2H Model Input Changes for 75% Penetration Sce narios HOMER Input Summary File name: 2A PEC624 Adv Battery Low Learning, High Costs with Grid 30c r1.hmr File version: 2.81 Grid Rate Power Price Sellback Rate Demand Rate Applicable $/kwh $/kwh $/kw/mo. Rate 1 0.3 (or 0.4) 0 0 Jan-Dec All week 00:00-24:00 Purchase capacity: 1,000 kw Sale capacity: 1,000 kw Generator control Check load following: Check cycle charging: Yes No Allow systems with multiple generators: Allow multiple generators to operate simultaneously: Yes Yes Allow systems with generator capacity less than peak load: Yes Constraints Maximum annual capacity shortage: 0% Minimum renewable fraction: 75% Page 77 of 79

Scenarios 3A to 3H Model Input Changes for 50% Penetration Scenarios HOMER Input Summary File name: 2A PEC624 Adv Battery Low Learning, High Costs with Grid 30c r1.hmr File version: 2.81 Grid Rate Power Price Sellback Rate Demand Rate Applicable $/kwh $/kwh $/kw/mo. Rate 1 0.3 (or 0.4) 0 0 Jan-Dec All week 00:00-24:00 Purchase capacity: 1,000 kw Sale capacity: 1,000 kw Generator control Check load following: Check cycle charging: Yes No Allow systems with multiple generators: Allow multiple generators to operate simultaneously: Yes Yes Allow systems with generator capacity less than peak load: Yes Constraints Maximum annual capacity shortage: 0% Minimum renewable fraction: 50% Page 78 of 79

9 Appendix B PV Module Data Sheet Page 79 of 79