Anexos

While both HOMER and RETScreen can perform economic analyses, an explicitly clear economic model was developed in a simple spreadsheet. This was to ensure all unique attributes of the various renewable energy technologies, policies, emission calculations, and biomass growth data were

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able to be re-modelled with an economic overlay, and verified for both assumption and formulae error to enable subsequent modification/duplication by third parties. The spreadsheet, referred to in this research as “the model”, incorporated the technical performance output data from RETScreen, HOMER, and peer reviewed literature to increase flexibility for the feasibility studies. The model incorporated capital expenditure cost calculations including (but were not limited to):

• loans;

• site preparation and equipment modification etc.);

• operating cost components (including, but not limited to maintenance, replacements, fuel/electricity costs etc.), and;

• the value of the remaining systems post decommissioning.

The model incorporated current market prices (and in some cases additional estimates) of both energy and carbon prices, projected over the 15 year project lifetime. Each feasibility study contained a number of assumptions and included a discount rate, detailed in each respective scenario, a discount rate of 11% and an inflation rate of 3% and was used for the analyses. This resulted in a real discount rate of 8%. Whilst rural infrastructure investments generally use a slightly lower real discount rate, commercial investments often use much higher real discount rates. Therefore, a flat real discount rate of 8% over the 15 year investment reflected a balanced (and common) approach to determine the

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economic value of the modelled systems. (See section 5.2.5 “Discounted Cash Flow (DCF) Analyses” for a detailed discount rate justification).

Conventional economic methods, such as Net Present Value (NPV), its negative, the Net Present Cost (NPC), and internal rates of return (IRR) (when possible), were used to create a simple economic analysis model suitable for private citizens and businesses to quantify benefits and costs over time. These economic methods were chosen as they are well established. For example, the NPV method was applied to financial investments by Simon Bruges as early as 1582, and bond tables with the equivalent of the IRR were in use by the latter half of the 19th century (Parker 1968). However, such methods are not without limitations, as the most probable NPV for a project (even with a sensitivity analysis) does not recognise the asymmetric probabilities associated with each variable (Slater, Reddy, and Zwirlein 1998). In addition, IRRs cannot be calculated when there is no positive cash flow (Ljungqvist and Richardson 2003). However, a simulation and scenario approach can explicitly recognise some asymmetries and their effect on the NPV calculation to demonstrate the project’s upside potential as well as downside risk (Slater, Reddy, and Zwirlein 1998), although this research only modelled a very limited number of possible simulations and scenarios.

The controversial nature of assumptions about adaptation behaviour of large numbers of disaggregated institutions and complications of projected

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technological developments must not be ignored by modelling analysis (Schneider 1997). As such, the regionally specific bottom-up adaptation and mitigation potential assessment models used in this analyses are able to account for many of the detailed local features and constraints (including ecological, institutional and landowner-behavioural), in addition to providing scope for variable assumptions, econometrics applied, and flexible baselines (Intergovernmental Panel on Climate Change 2007).

As an example of the detail of assumptions required for the capital and ongoing cost estimations for small-scale renewable energy systems, there are commonly choices between “high-end” and “low-end” technologies in terms of quality and reliability. Furthermore, the modelled cost analyses were based on a system lifetime of 15 years. However, for a PV module the assumed lifetime of 15 years was likely to be an underestimate. Conversely, the lifetime of the inverters and battery banks were also modelled as 15 years, which is likely an overestimate, based on recent research under Australian conditions (McHenry 2009). Despite these limitations, an iteratively^a balanced approach was chosen for each simulation and scenario based on the author’s knowledge of such small-scale renewable energy systems and carbon sequestration options.

a The use of the word ‘iteratively’ in this sense is based on the mathematical definition where a number of parameters are calculated to approximate a result. In this case, to achieve an ‘average’ cost, quality, and replacement interval.

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The model assessment is designed to obtain results relevant to the effect of farm level output and income. This is the primary reason behind the further development of the “market mitigation” and “market adaptation” potential concept – to obtain results directly relevant to agriculturalists. This includes the input of first-order crop level production impact (or likewise energy produced/offset, and emissions net produced/mitigated), to the second-order market values at the farm level. This stepwise analysis allows for consideration of nuances (such as market prices or policy reform/development) that will likely influence the decision-making process, including Australia’s naturally variable climate, sectoral emission profiles, and their respective value and vulnerability.

Agricultural lands in the SW of WA are generally supplied with electricity from the regional network (known as the SWIS – Southwest Interconnected System), using the government-owned retailer, Synergy’s Home Business Plan (K1) tariff.

The daily supply charge and the cost of the first 20 kWh are identical with the Synergy Home Plan (A1) tariff, tailored for non-agricultural domestic useres.

However, electricity consumption above 20 kWh per day is supplied at the Synergy Business Plan (L1) tariff rate, which is tailored for non-agricultural small businesses (Synergy 2010). Each of these tariffs has significantly increased in recent years (Table 1.1).

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Charges per day Pre 1/07/’09 Post 1/07/’09 Post 1/4/’10 Post 1/7/’10 Supply 25.57 ¢ day^-1 32.33 ¢ day^-1 34.75 ¢ day^-1 38.23 ¢ day^-1

< 20 kWh 13.94 ¢ kWh^-1 17.61 ¢ kWh^-1 18.93 ¢ kWh^-1 20.83 ¢ kWh^-1

> 20 - < 1650 kWh 17.47 ¢ kWh^-1 22.08 ¢ kWh^-1 23.73 ¢ kWh^-1 26.11 ¢ kWh^-1

Table 1.1: Summary of K1 tariff charges (GST inclusive). Source: (Synergy 2010;

Frontier Economics 2009).

The WA Renewable Energy Buyback Scheme (REBS) is available for renewable energy grid-connected systems on the SWIS of capacity between 500 W and 5 kW. REBS is calculated on the net import total over the billing period, at a tariff equal to the purchase rate minus GST. However, this was amended in 2010 to AUD0.07 per kWh on the SWIS, while the other major government-owned retailer in WA, Horizon Power which operate off the SWIS, remain at the equal rate minus GST. Therefore, on the SWIS the Synergy REBS renders the value of electricity exported into the grid from residential homes at around one-third of the value of electricity sales to homes. To be eligible for REBS, the client must be on the A1 or SmartPower (a time of use variable) tariff, and residences on the K1 tariff are ineligible. Similarly K1 clients are ineligible to receive the WA feed-in tariff (FiT). As the K1 tariff supply structure (under 20 kWh) reflects the A1 tariff, it may be perceived as inconsistent that K1 tariff customers are ineligible for REBS. Therefore, K1 customers, many of which are located in rural areas, are unable to receive equivalency for exported and imported electricity to and from the SWIS network akin to A1 or SmartPower customers.

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Rural regions are often located in the “fringe-of-grid” areas that are known to require additional voltage and frequency improvement measures. Decreased net demand in “fringe-of-grid” areas may become a suitable adaptation alternative to additional utility generation capacity, or network extension in some circumstances. However, this would require further analyses and was outside the scope of this research.

In the model, electricity exports to the SWIS received a zero economic return due to the K1 tariff REBS ineligibility. Each system performance simulation was designed to supply electricity to a agricultural homestead in real-time (15 minute simulated intervals) only displacing electricity imports. The simulation calculates electricity exports from the small-scale homestead generation system to the network, although it was given a zero economic value in the model.

Therefore, any economic benefits of small distributed generators providing capacity (and possibly voltage and frequency control ancillary services) in the simulation and model will be captured by the SWIS network operator (Western Power), or various other generators under the auspice of the SWIS System Management. The model represents this as an opportunity cost at the expense of K1 customers. This particular model is relevant to around 13,000 customers on the K1 tariff, consuming an estimated 130,000 MWh each year on average, which is also increasing (Office of Energy 2009).

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