1. business and industrial
2. energy
3. electricity

Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
3-Party Covenant Financings of Solomon Islands Power Projects
Paul Simshauser, Leonard Smith, Patrick Whish-Wilson & Tim Nelson 
Level 6, 144 Edward Street
Brisbane, QLD, 4000.
January 2015
Abstract
The correlation between suboptimal economic and health outcomes and a lack of
affordable electricity is well established. By any standard, Solomon Islanders face
extraordinarily expensive electricity tariffs – currently set at 96c/kWh compared to
26c/kWh in Australia – making them amongst the highest in the world. Power is supplied
by a fleet of diesel generators reliant on imported liquid fuels. In this article, we model
the 14,100 kW power system on the island of Guadalcanal and demonstrate that by
investing in a combination of hydroelectric and solar PV generating capacity, power
system costs and reliability can be improved marginally. However, when we model a 3Party Covenant Financing structure involving the Commonwealth of Australia, electricity
production costs fall by 50%, thus resulting in meaningful increases in consumer welfare.
We also find 3-Party Covenant Financing of strategic energy projects in the Pacific to be
consistent with the underlying tenets of the Australian Government’s ‘new aid paradigm’.
Keywords: Renewable Energy, Electricity Prices, Project Finance, Foreign Aid.
JEL Codes: D61, L94, L11 and Q40.
1.
Introduction
The Solomon Islands consists of six major islands and 900 smaller islands1 with a total
population of around 580,000 people. A large proportion of the population rely on subsistence
agriculture and the associated ‘informal economy’ while 20% are considered ‘urban dwellers’
(World Bank, 2012). The Solomon Islands is one of the poorest countries in the South Pacific on
a GDP per capita basis – Official Development Assistance (i.e. foreign aid) was 22% of Gross
National Income in 2001 yet by 2010 had increased to 61% (World Bank 2015).2 The Solomon
Islands has thus progressed from being moderately reliant on foreign aid to being the second most
aid-dependent country in the world (Schwarz et al. 2011; Hayward-Jones, 2014). Juxtaposed to
this is the fact that it is also among the most resource-rich countries in the Pacific (Allen 2011;
Gouy 2011; Hameiri 2014; Moore 2004). Some of these natural resources are however being
depleted at an unsustainable rate – timber being a primary case in point (Allen 2011; Hameiri
2014; Nanau 2014).
Our specific interest is Solomon Islands electricity prices, which are among the highest in the
world (IRENA 2013; URA 2013). Current residential tariffs in Australia are approximately
26c/kWh whereas prices in the Solomon Islands are nearly four times higher at 96c/kWh
(business tariffs are even higher at 100-103c/kWh).3
One of the more significant challenges facing the Solomon Islands and other Small Island
Developing States of the Pacific is rural electrification. Dornan (2014) has argued, with

Paul Simshauser is Chief Economist at AGL Energy Ltd and Professor of Economics at Griffith University. Leonard Smith is an
Economist at AGL and a PhD (Economics) candidate at the University of Queensland. Patrick Whish-Wilson is a Senior Economist
at AGL Energy. Tim Nelson is Head of Economics & Sustainability at AGL Energy Ltd and Adjunct Associate Professor of
Economics at Griffith University.
1
About 350 of these Islands are populated (IRENA, 2013).
2
The statistics relating to the Solomon Islands have been taken from the World Bank’s ‘Development Indicators Database’ and can be
accessed at http://data.worldbank.org/country/solomon-islands. Accessed Jan 15, 2015.
3
See tariffs schedule at www.siea.com.sb for details.
Page 1
considerable justification, that off-grid remote areas deserve more policy attention than main
electrical grids given that around 70% of Pacific Island households do not have access to
electricity at all (a level equivalent to sub-Saharan Africa). For rural areas, small-scale solar
photovoltaic (PV) and battery configurations represent a genuine opportunity to substantially
enhance electrification, and in many instances at considerably lower cost than extending
grids. However, our focus is on the Solomon Islands’ primary power system on the island of
Guadalcanal which houses the capital and commercial hub, Honiara where 80% of the urban
population lives. Apart from enabling us to keep our analysis tractable, we focus on the main
grid supplying the commercial hub due to its strategic importance to the nation.
Our analysis of Guadalcanal’s 14,100 kW power system suggests it is substantially
undercapitalised. Severe supply disruptions were experienced as recently as 2013.4 This, in our
opinion, is a problem worth solving because an affordable and reliable supply of electricity is
fundamental to the capacity and potential of a nation’s human capital. Any improvement in
reliability, cost, or both would result in unambiguous improvements in welfare. Electricity is a
desirable form of energy due to its wide-spread application and uniformity. Moreover, it unlocks
the ability to utilise technology and equipment that science and innovation have created – which
in turn is a first step to generating surpluses of labour and human capacity.5 Once surpluses of
labour and human capacity exist, endemic economic and social progress can occur (Ferguson et
al. 2000; Lewis 1954; Pueyo et al. 2013). We tend to lose sight of this potential in developed
nations because electricity has long been considered an essential service.
Given the electricity industry is the one of the world’s most capital-intensive, unsurprisingly,
developing country governments find it difficult to promote and deliver electrification. Aside
from scarce capital resources, a lack of technical capacity and human capital required to deliver,
operate and maintain a power system are significant obstacles (Chakrabarti & Chakrabarti 2002).
Ironically, the technical capacity required to develop and run an efficient electricity system is in
part fostered via investment in human capital and education, which in turn is stimulated by
electrification – thus contributing to a circular reasoning. This circular reasoning has produced
substantial research in the field (Jacobson 2007; Mulder & Tembe 2008; Daka & Ballet 2011;
Khandker et al. 2013; Sapkota et al. 2013). The Solomon Islands faces such challenges with
IRENA (2013, p.3) noting the Ministry of Energy has an extensive role ‘but staffing levels and
financial allocations are inadequate to carry out these functions’ while the Solomon Island
Electricity Authority is said to have had a ‘long history of under-investment, insufficient
resources and limited staff capacity’.
In this article, we use dynamic power system models to analyse what can be done to reduce the
high cost of electricity supply in the Solomon Islands given it is a requisite input for
technologically advanced systems (Ferguson et al. 2000; Niu et al. 2013; Winther 2013). The
focus of this article is therefore to identify what policy interventions are capable of making
profound changes to the cost and the reliability of wholesale electricity production. We look to
transition Guadalcanal from a diesel-fired power system to a renewables-dominated system
designed to sustainably develop the Solomon Islands’ abundant natural resources. We envisage
new renewable capacity to be developed under 3-Party Covenant Financings involving the
Solomon Islands Electricity Authority, Private Power Developers and Project Banks, and the
Commonwealth Government of Australia.
Before proceeding, it is important to note that our analysis has a number of limitations. First, we
do not deal with the problem of rural electrification. Second, we focus on generation costs –
4
See Solomon Islands Electricity Authority 2013 Annual Report for details.
Access to electricity enables children to study after dark, water to be pumped for crops and allows food and medicines to be
refrigerated. More concisely, it can facilitate human capacity surpluses and promote faster and more efficient systems of doing
business.
5
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
distribution network costs are excluded from the analysis. Third, we do not consider all plausible
projects (e.g. geothermal project options). And finally, we do not examine essential
preconditions to project development such as property rights – a particularly extensive and
sensitive topic as Meinzen-Dick & Pradhan (2002) explain.
This article is structured as follows: in Section 2 we present a Review of Literature and Section 3
outlines our data set. Section 4 explains our modelling approach. Sections 5 and 6 present our
plant and system-simulation results, respectively. Policy recommendations and concluding
remarks follow.
2.
Review of Literature
At the heart of this research is an objective to radically reduce the wholesale cost of electricity,
thereby reducing the incidence of energy poverty. During the course of our analysis we have
formed a view that active policy intervention is quite essential. However, this raises questions
regarding the sustainability of foreign aid6 and other associated programs. For a range of reasons,
foreign aid is not always effective. It can produce exogenous reliance or constrain endogenous
growth and in some instances has created as many problems as it has solved (see for example
Boone 1996; Lensink & White 2001; Easterly et al. 2003; Clemens et al. 2012). The policy
intervention we envisage to maximise consumer welfare in the Solomon Islands is 3-Party
Covenant (3PC) Financings, initiated by the Commonwealth Government of Australia as a
constituent component of Australia’s overall development assistance program. Accordingly, our
review of relevant literature necessarily includes (1) energy poverty, (2) theories of development
and growth, (3) the cost of capital and (4) 3PC Financing.
2.1
Energy Poverty
Energy poverty can be defined as a lack of access to a modern energy service (Cecelski 2000).
To be clear, energy poverty is distinctly different from fuel poverty. Fuel poverty – an issue
which AGL Working Paper No.17 canvassed in considerable detail – relates solely to the issue of
affordability (Foster et al. 2000; Simshauser et al. 2011; Boardman 2013). From a policy
perspective, energy poverty is a profoundly more serious problem because it relates to suboptimal
(or the absence of) energy infrastructure. In the developing world, energy poverty remains
widespread (Kaygusuz 2012).
Energy poverty of Australia’s near neighbours is worthy of policy attention because the wellbeing of their populations is negatively affected. Under such conditions, one can expect a sharp
rise in the use of fuels with larger environmental footprints and a disproportionate amount of
time spent collecting fuel to meet the most basic of human needs (Pereira et al. 2010; Pachauri &
Spreng 2011; Sovacool 2012). Although developed countries like Australia are beginning to
decouple energy consumption from economic growth through structural change and energy
efficiency, this is not the case in developing countries. In poorer countries, there remains a strong
direct relationship between electricity consumption and economic development (Ferguson et al.
2000; Pereira et al. 2010; Pueyo et al. 2013; Niu et al. 2013). The relationship between electricity
consumption and economic growth thus has a prolific literature which is formally summarised in
Pueyo et al. (2013) and Al-mulali et al. (2014).
There is a broad consensus that access to affordable electricity is well correlated to ‘greater
prosperity’7 (Davis 1998; Ferguson et al. 2000; Cecelski 2000; Cabraal et al. 2005; Bernard 2010;
6
Total finance flows to developing countries tell a compelling story. In April this year, the OECD reported that Official Development
Assistance totalled US$134 billion, remittances were around US$400 billion, private capital flows to developing countries were
almost $890 billion, and philanthropic aid, a relatively new source of development finance, totalled US$70 billion
7
Establishing causation with respect to electricity, wealth creation and economic development seems to be analogous to the ‘chicken
or the egg’ debate. Extremely comprehensive reviews of the literature exploring the link between poverty and access to electricity are
holistically covered in Pueyo et al (2013).
Page 3
Cook 2011; Niu et al. 2013; Pueyo et al. 2013; Halkos & Tzeremes 2014). Electricity provides
services that meet many basic human needs, particularly heat, motive power (e.g. water pumps
and transport) and lighting; while commerce, modern healthcare, education and the
communications industries are dependent on access to electricity (Yang & McCall 2014).
Consequently, there is a strong statistical correlation between electricity consumption, the health
of a population and many other social indicators (Martins 2005; Duflo et al. 2008; Niu et al.
2013). Indeed, energy poverty has a direct relationship with other indicators of poverty such as
infant mortality, illiteracy, life expectancy and total fertility rate (World Bank 2014). Figure 1
plots the relationship between the Human Development Index, a measure of broader human
utility, and Energy Development Index, a measure of energy access and affordability.
Figure 1:
Human Development Index and Energy Development Index
Sources: (IEA 2012; World Bank 2014)
It would therefore seem definitive that access to affordable and reliable electricity supplies
facilitates social and economic development (Davis 1998; Ferguson et al. 2000; Modi et al. 2005;
Niu et al. 2013; Pueyo et al. 2013; Winther 2013).
Theories of Growth and Development
Before exploring options that increase the reliability and substantially reduce the cost of
electricity supply to assist a nation move forward, we believe it is helpful to properly grasp the
evolution of the growth literature in the context of refining how policy intervention (i.e. foreign
aid) might best be packaged. Doing so provides a sense of place with respect to achieving
sustainable growth, how it is modelled, and why electricity provides a foundation for a more
technologically advanced world.
2.2
As Todaro (1989) explains, the literature on economic development from the 1950s onwards has
been dominated by four major and competing strands of thought, viz. linear growth, patterns of
structural change, international dependence models and neoclassical free market counterrevolution.8 The scholarly logic of the 1950s focused on the concept of stages of economic
8
Linear growth theory was replaced in the 1970s with two contrasting economic schools of thought. The theories and patterns of
structural change relied heavily on statistical analysis and early econometrics, while international dependence theories viewed
underdevelopment in terms of global power struggles and emphasised institutional and political constraints to growth (Todaro, 1989).
Throughout much of the 1980’s a neoclassical counter-revolution in economic thought occurred emphasising the benefit of promoting
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
growth and the interplay between national savings, infrastructure investment and foreign aid.
Linear growth theory presumed that when the right mix of savings, investment and foreign aid
prevailed, the economy of a developing nation would begin to prosper. Thus ‘development’
became synonymous with rapid aggregate economic growth. In some respects, modern day
China could be thought of as an example of linear growth theory.
There are two models that we consider worth reviewing in the context of electricity industry
development, viz. the seminal works of Rostow (1959) and his Stages of Economic Growth, and
Solow’s growth model (Solow 1956). These two models provide a ‘foundation logic’
underpinning the thinking behind our subsequent 3PC Financing policy prescription. Rostow’s
(1959) model of economic growth and development basically comprises five stages of varying
lengths, which we have illustrated in Figure 2.
Figure 2:
Rostow’s Model of Economic Growth & Development
The relevance of Rostow is that as a structuralist model of economic growth, it emphasizes that
‘Stage 3 – Take Off’ will occur when certain individual sectors become established.9
Gerschenkron (1962) stressed that while stages of growth may be linear, growth within stages is
not – they occur in step changes. This conclusion would lead us to surmise that given available
technologies, growth-orientated policies that focus on areas beyond agriculture would seem
sensible. Indeed Gerschenkron (1962) argued that rapidly advancing areas should be the focus,
further emphasising the importance of technology transfer. The fact that large areas of Africa
moved from no communications infrastructure to mobile phones (i.e. in one step) illustrates our
point. Regardless, it is difficult to imagine ‘Stage 3 Take-Off’ occurring at any level of
sustainable success in the absence of a reliable and affordable supply of electricity.
Solow’s Growth Model (1956)10 emphasizes growth in per-capita output as a result of capital
accumulation and technological progress, and that once an economy reaches a steady state, perfree markets and minimal government intervention and can be thought of as a sub-component of the Washington Consensus and the
logic that underpinned major tenets of globalisation (Dollar & Kraay, 2001; Firth, 2000; Ghemawat, 2007; Kaplinsky, 2013)..
9
Credence for this model was rooted in the work of David Ricardo's (1815, 1817) comparative advantage musings based in their
infancy on an argument for free trade and ‘comparative costs’. This became one of the important concepts in the theory of
modernization in social evolutionism.
10
Solow’s (1956) seminal neoclassical growth model subjected capital accumulation to diminishing returns. Introducing
“Technological Progress” the aggregate production function becomes: 𝑌 = 𝐾 𝛼 (𝐴𝐿1−𝛼 ). The steady growth state was the homeostatic
or state of equilibrium within the economy characterised by the fact that it was absent of technical change. However this assumption
does not seem to be realistic with the power of retrospective inquiry. As a consequence, his model regarded long-run growth as an
exogenous parameter that could not be influenced by policy. A country with a higher saving rate will experience faster growth, e.g.
Singapore had a 40% saving rate in the period 1960 to 1996 and annual GDP growth of 5-6%, compared with Kenya in the same time
period which had a 15% saving rate and annual GDP growth of just 1%. This relationship was anticipated in the earlier models and is
retained in the Solow model; however, in the very long-run capital accumulation appears to be less significant than technological
innovation in the Solow model.
Page 5
capita output growth is primarily driven by exogenous (external) technological progress.11 The
model treats foreign aid or exogenous capital quite differently. Exogenous inflows invariably
increase the short-run capital stock of a developing country (for example, in the form of bridges,
railroads, roads, power systems and factories). This in turn will be reflected in short-run
increases in GDP. However, if the underlying fundamentals of the aid-recipient country have not
developed sufficiently, then depreciation of the capital stock may begin to exceed growth in
infrastructure investments and the maintenance of sunk capital infrastructure. In other words, the
short-run increase in the capital stock above a steady state equilibrium can be followed by longrun ‘excess depreciation’ (e.g. civil, mechanical and electrical plant falling into a state of
disrepair – potholes in roads, power systems deteriorating). Under conditions of inadequate
national savings, such problems will not be fixed and the aid-recipient country will be pushed
back towards an underlying ‘steady state’ level of capital and GDP (Cowen & Tabarrok 2009).
Such dynamics are clearly more than a theoretical possibility – remote Solar PV applications
funded by foreign aid and installed in the Solomon Islands during the 1990s eventually failed due
to inadequate maintenance (IRENA, 2013).
How then might foreign aid best be packaged when contemplating electricity infrastructure to
ensure that a donor country’s scarce resources meets its objective? In our view, these older
models of growth and development have a way of framing how best to prescribe policy
interventions for a power system. While technological change is now regarded as endogenous,
the capacity to extract maximum productive efficiency from technology applications, or the lack
thereof, can be purely exogenous.
Developing country governments often rely on foreign aid and other forms of external assistance
along with exogenous economic forces to progress large strategic infrastructure projects
(Georgopoulou et al. 1997; Gibson et al. 2005). However, without the internal capacity to
develop, operate and maintain installed capacity the economic system may self-equilibrate as
suggested by Solow (1956). This means there are likely to be significant long-term benefits from
private sector participation in developing power systems given the technical and financial
dynamics that underpin electricity infrastructure projects (Bond & Carter 1995; Painuly et al.
2003; Sarkar & Singh 2010; Bhattacharyya 2013; Wilson et al. 2014). But given the capitalintensive character of the electricity industry, in our opinion such a statement necessarily comes
with a caveat: private sector participation is desirable subject to the cost of capital that can be
practically achieved. In order to optimise the cost of capital, and simultaneously minimise the
prospect of foreign aid being sub-optimally invested, we consider a 3PC Financing policy to be
an important delivery mechanism.
2.3
The Cost of Capital
Weighted Average Cost of Capital (WACC) calculations (i.e. average cost of debt and equity
capital deployed for a given investment) are used for performance measurement and investment
decision making purposes in a broad spectrum of contexts. However, while routinely determined
in developed countries12, WACC calculations can be particularly difficult to derive in developing
nations. A number of studies have shown that the Capital Asset Pricing Model13 (a model used to
derive equity cost estimates in mature economies) frequently breaks down in developing
countries due to a lack of historical markets data, deep market inefficiencies, the different nature
of risks involved, and bad statistical properties of any available time series. Seminal works of
literature in this regard include Harvey (1995), Bekaert & Harvey (1995), Diamonte et al. (1996),
11
The Solow growth model can be described by the interaction of five basic macroeconomic equations including (1) Macroproduction function, (2) GDP equation, (3) Savings function, (4) Change in capital and (5) Change in workforce.
12
Simshauser (2014) provides one such example by calculating the Weighted Average Cost of Capital for merchant power generation
in Australia under atypical capital market conditions.
13
The Capital Asset Pricing Model is an economic estimation model developed independently by Sharpe (1964) and Lintner (1965)
and is used extensively by firms, stock analysts, regulators and policymakers to produce estimates for the cost of equity.
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
Erb et al. (1996), Godfrey & Espinosa (1996) and Estrada (2000). Recent additions have been
made by Damodaran (2003, 2012) although it would seem that the literature in this field was
essentially hi-jacked by the Global Financial Crisis and its consequences for developed country
capital markets.
In developing countries, the cost of capital is invariably elevated with risk premiums reflecting
negative investor sentiment and high risk metrics. The greater the perceived country risk, the
more encouragement foreign capital requires to invest.14 The cost-consequence is ultimately
borne by a developing country’s population and has the effect of reducing welfare.
The usual proxy used to determine the perceived financial risk of a sovereign nation is to compare
country credit ratings, spreads on government bonds or spreads on Credit Default Swaps
(Damodaran 2003, 2008, 2012). In Figure 3, we present total equity risk premium estimates (yaxis) versus country credit ratings (x-axis) using Damodaran’s (2015) latest data set. Equity
returns are calculated by deriving the risk premium for mature equity markets (US S&P500) plus
risk premiums for individual countries based upon their 10 year Credit Default Swap spreads,
adjusted by a factor of 1.5x which at the time of writing reflects the relative volatility of equity
markets and the market for Credit Default Swaps (Damodaran 2013).
Figure 3:
Total equity risk premiums vs country credit rating
(Damodaran 2015), Stern School of Business, New York University.
No such calculation is possible for the Solomon Islands. The sovereign government is not rated,
there is no market for Solomon Island bonds and therefore no credit default swap spread exists.
Unsurprisingly, without policy intervention the WACC will be elevated for capital-intensive
infrastructure projects characterised by asset specificity and asset immobility – with a cost of
equity likely to be 25+% and similarly elevated debt capital costs (15+%). This will adversely
affect the development of capital-intensive infrastructure projects in the Solomon Islands in the
same way as other developing nations with analogous dynamics (see Doh & Ramamurti 2003;
Ramamurti 2003; Ramamurti & Doh 2004).
The immaturity of capital markets in countries such as the Solomon Islands and the difficulty in
deriving even the most basic form of benchmark calculation helps explain why attracting
14
Apart from elevated risk premia, encouragement frequently takes additional forms including additional tax incentives or reduced
royalty rates and favourable contracts.
Page 7
investment can be so difficult (Sill, 2000). It also helps to explain why investments are priced at
what we would describe as striking risk premiums in unrated sovereign nations. There must also
be some element of circularity in this – if no formal benchmark exists, capital flows will face
substantial frictions in the first place, which limits the prospects for a formal benchmark being
formed in the future.
2.4
3-Party Covenant Financings
Debt finance is crucially important to capital-intensive infrastructure projects. Debt instrument
choice is most strongly linked with the credit history of the issuing entity and the current credit
quality of the issuer (Denis & Mihov 2003). Firms with the highest credit quality exhibit a strong
preference for public debt, while firms with credit ratings towards the middle of the spectrum
tend to prefer bank debt, whereas those at the bottom of the credit rating spectrum borrow from
non-bank private sources and at high cost (Berger & Udell 1998; Carey et al. 1998; Berger &
Udell 2006). This pattern broadly supports the model of Diamond (1991) and Diamond & Rajan
(2001) in which borrowers with high credit ratings earn rents from their reputations with lenders.
The existence of such rents is also consistent with observations made within Graham & Harvey
(2001) that managers place a high priority on maintaining their existing credit rating. At the other
end of the spectrum, non-bank private debt plays a unique role in accommodating the debt
financing needs of entities with low credit quality (Kahan & Tuckman 1993).
Given the central importance of the cost of capital to electricity infrastructure projects, we
explore the option of 3PC Financings. 3PC Financings are similar to a credit wrap designed to
combat otherwise very high premia on loan facilities (credit wrapping is the enhancement of a
debt obligation by higher credit quality issuers).15 Wrapped financings have been identified in
numerous variations, contexts and semblances over time (Smith Jr & Warner 1979; Kahan &
Tuckman 1993; Diggle et al. 2004; Rosenberg et al. 2004;). Diggle et al. (2004) observe that
liquidity costs are always borne by investors, whereas issuing costs are absorbed by issuers. If
the credit wrapper is a high credit-rated Federal Government, it will have the effect of adding
significantly to the liquidity of any debt issue, and clearly, add to the credit quality of any new
issue and in turn reduce the cost of debt (Bekaert & Harvey 1995).
Diggle et al. (2004) examine Australian Commonwealth Government credit wraps to Local
Government Authorities with a focus on making capital-intensive infrastructure projects more
economically viable. The 3PC Financing structure we envisage is based on work undertaken by
Rosenberg et al. (2004) at the John F Kennedy School of Government (Harvard) relating to the
rapid deployment of capital-intensive power generating technologies.16
3PC Financing as originally envisaged entailed a structured power project arrangement between
the US Federal Government, a State Public Utility Commission and a power project developer
with an aim of dramatically reducing the cost of capital for capital-intensive (low emission)
power projects. How this would be achieved centred around financially engineering the cost and
level of debt raised through reorganising the allocation of power project financial risk. The
concept is analogous to Monoline Insurers17 wrapping the bond issues of Australian regulated
15
The concept of debt, credit wrapped by Federal governments is not a new one to global capital markets. In the US, the largest
mortgaged backed issuers, the Federal National Mortgage Association (Fannie Mae); the Federal Home Loan Mortgage Corporation
(Freddie Mac) and the Federal Home Loan Bank System are known as Government Sponsored Enterprises (GSEs). The difference is
that the underlying asset underpins the loans and acts as the collateral Michael Regan, Jim Smith, and Peter E. D. Love, 'Impact of the
Capital Market Collapse on Public-Private Partnership Infrastructure Projects', Journal of construction engineering and management,
137/1 (2010), 6-16..
16
Rosenberg et al. (2004) were examining Integrated Gasification Combined Cycle and Carbon Capture & Storage power projects.
17
Monolines are insurance companies that provide guarantees to issuers of debt, typically in the form of a credit wrap, which has the
effect of enhancing the credit rating of the debt (thereby reducing the risk premiums in line with the higher credit rating achieved on
issue). In Australia between 2003-2007, more than $6 billion of debt issued by Australian electricity and gas utilities (e.g. United
Energy, Powercor, Citipower, ETSA Utilities, Basslink, ElectraNet, Envestra) was wrapped by AAA- rated monoline insurance
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utilities – the ultimate impact of which is that lenders have an additional recourse to the credit
wrapper (i.e. the Monolines) and the issuer (i.e. Regulated Utility) achieves a lower cost of funds
(Chava & Roberts 2008).
Our derivation of 3PC Financing has been abstracted and applied to the Solomon Islands with the
primary intention of reducing country, credit and illiquidity risk premiums that would otherwise
add to relevant WACC calculations. However, our derivation of 3PC Financing has one crucial
difference to that of Rosenberg et al. (2004) – we envisage that it is the Power Purchase
Agreement (PPA) rather than the project debt that is the subject of a credit wrap. That is, the
Commonwealth of Australia would credit wrap a long-dated (and otherwise un-rated) Solomon
Islands Electricity Authority PPA. The reason for this is twofold; (1) approved revenue streams
for banking energy infrastructure projects is of paramount importance (Stelwagon 1996; Joshi
2002; Doh & Ramamurti 2003; Kundra 2008; Simshauser, 2010; Nelson & Simshauser, 2013;
Simshauser & Ariyaratnam 2014), and (2) to ensure responsibility for power plant construction
risk and operating risk remains with project proponents, where it is best managed.
Through a credit-wrapped PPA, 3PC Financing will greatly improve the prospects for a project
financing on favourable rates and terms. And, we believe the use of project finance to be of vital
importance to the success of a 3PC Financing policy in the context of growth and development
theories. Our derivation of a 3PC Financing policy necessarily involves non-trivial capital
commitments by private power project developers and a syndicate of Project Banks. The benefit
of this is that it brings all of the technical, commercial and due diligence capabilities to the
process of developing and screening long-lived energy infrastructure projects, thereby reducing
the risk of non-viable projects proceeding (Brealey et al. 1996; Sader 2000; Ramamurti 2003;
Yamin & Sinkovics 2009; Chaudhuri & Mukhopadhyay 2014; Pi & Zhou 2014).
Using direct foreign aid to fund or part-fund power projects could lower power production costs
even further than a 3PC Financing. But a sole focus on exogenous sources of growth for a
developing economy risks potential reliance and more critically, Type II investment error (i.e.
accepting a project that should be rejected). This is because a plant funded by foreign aid or
through an issue of Australian Government Bonds is unlikely to be subjected to the level of
commercial scrutiny that is achievable under other structures. A Project Finance on the other
hand results in a concentrated ownership of syndicated bank debt which encourages risk-averse
Project Banks to devote considerable resources to evaluating and minimising plant construction
and operating risks prior to commitment (Brealey et al. 1996; Nelson & Simshauser 2013).
Moreover, our view is that Australian policymakers may be better to focus on a broader strategy
that recognises the importance of endogenous forms of growth given that the Solomon Islands is
now the second most aid-reliant country in the world.
3PC Financing has the effect of creating a ‘contingent liability’ for the Australian Government
that would only crystalize in the event of a PPA default, which in turn reflects the credit risk of
the Solomon Islands Electricity Authority and its existing consumer base. The Authority has
about 11,000 residential and business customers, but modern technology has materially altered
the usual credit risk of its portfolio of electricity consumers. About 80% of the Solomon Islands
Electricity Authority’s residential customers have ‘prepayment meters’ as distinct from the usual
electricity industry practice of quarterly billing cycles post-consumption.18
companies (e.g. Ambac, FSA, XLCA, MBIA). At the time, this credit enhancement had the effect of reducing spreads from 100+bps
to less than 40bps over swap rates.
18
Prepaid electricity meters were invented in South Africa with an objective of improving cost recovery and minimizing billing costs
for domestic users (Tewari & Shah 2003). Evidence and examples of prepayment within different global contexts are provided by
Tewari & Shah (2003), Nefale (2004), Casarin & Nicollier (2008), van Heusden (2009) and Baptista (2013).
Page 9
The benefit to the Solomon Islands arising from 3PC Financing should thus form part of
Australia’s aid budget. 3PC Financing has the effect of shifting the source of aid funding from
fiscal account surplus/deficit (i.e. cash outlays) to Balance Sheet (i.e. credit wrap). However, this
is not a “magic pudding” – 3PC Financing creates an asset-backed contingent liability and will
have the effect of reducing Australia’s own debt capacity by a commensurate amount, holding the
nation’s credit rating constant.19
This may prima facie appear to be a somewhat novel approach to the delivery of foreign aid. But
as the Foreign Minister recently noted, Australia is among the top 10 donor countries in the
OECD20 and therefore innovation of how Foreign Aid is delivered is important:
…Innovation will drive the way we deliver Aid. We have taken advice from the World
Bank and other likeminded aid agencies and this is ground breaking stuff for Australian
Aid. Over the next four years we will spend $140 million in trialing and testing
development innovations. Finding much more creative and clever ways to achieve better
results, thinking differently and being more entrepreneurial in our approach… (Bishop,
2014).
The Australian Government intends to trial new methods of delivering foreign aid and 3PC
Financing of strategic power projects appears consistent with this objective. Our subsequent
modelling demonstrates that a Commonwealth Government credit wrapped PPA in the Solomon
Islands substantially bolsters the economic case for renewable energy projects and reduces the
combustion of imported diesel fuels. Given the capital-intensive nature and strategic importance
of renewable energy infrastructure in a country otherwise reliant on imported diesel fuels,
innovative forms of financing become even more important (Sorrell 2004; Bergmann et al. 2006;
Wüstenhagen & Menichetti 2012).
3.
Power system load and plant assumptions
A variety of sources have been used to construct our model of the Solomon Island’s main power
system. Our 2014 hourly load data has been sourced reliably. But to be clear, our supply-side
assumptions are best described as ‘indicative only’. Publicly available data on the power system
was limited. It has been necessary to estimate the number, size and efficiency of existing and
future plant options from a variety of sources. For our purposes, all model inputs and outputs are
expressed in nominal 2014 Australian dollars.
3.1
Demand-side
Figure 4 provides an illustration of typical 2014 daily load curves (viz. average weekday, average
weekend, and top 5 ‘critical event days’) for the Guadalcanal system.
19
That is, one would expect Credit Ratings Agencies would look-through any credit wrap and account for it as a drawn loan facility
when assessing Australia’s country credit risk.
20
A$5 Billion a year for the next two years, increasing with CPI thereafter.
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
Figure 4:
Daily load curves in 2014 (average weekday, weekend, critical event days)
Source: SIEA, AGL Energy Ltd.
System load is relatively predictable (by comparison to Australian loads) reflecting the low
variation in ambient temperatures (average temp 27.2 o C, standard deviation 2.1o C) as Figure 5
reveals:
Figure 5:
Average electricity load by month in 2014
Source: SIEA, AGL Energy Ltd.
We estimate 2014 aggregate system final demand of 78,174,000 kWh with a 14,100 kW peak
demand and system load factor of 63.3%. Our estimate of aggregate final demand (represented
by a load duration curve) is illustrated in Figure 6.
Page 11
Figure 6:
2014 aggregate load duration curve
Source: SIEA, AGL Energy Ltd.
3.2
Supply-side
The plant servicing aggregate demand has a high cost because all generating equipment is fired
on imported diesel fuels ex-Singapore with fuel delivered approximately every three weeks and
stockpiled. This presumably compounds energy security risks. At the start of 2013, virtually all
installed capacity (i.e. reciprocating engines/diesel generators) was operating beyond design life
(i.e. the entire generating fleet was 20+ years old).21 However during 2013, 2 x 1,500kW peak
generators were installed at Honiara Power Station and tenders had been called for 2 x 2,500kW
generators at Lungga Power Station. In our base case scenario, we incorporate these new
generators along with a further five operating units ranging in sizes from 1,500kW to 4,200kW
(de-rated to 3,000-3,900 kW as publicly available data indicates). Table 1 presents our supplyside assumptions. The Marginal Running Cost of the diesel generating sets ranges from $290.91
- $462.20/MWh based on our weighted average 2014 diesel price of $28.32/GJ CIF22.
Table 1:
Existing Generating Fleet Dispatch Parameters
Plant
L1
L2
L8
H1
H2
L7
L10
L6
L5
Capacity Heat Rate MRC Availability
(kW)
(kj/kWh) ($/MWh)
(%)
2500
10299
290.91
99.0
2500
10299
290.91
99.0
3900
10915
308.30
85.0
1500
11703
330.56
98.0
1500
12673
357.97
98.0
3000
15078
425.89
85.0
3500
15078
425.89
85.0
2800
16364
462.20
80.0
1500
16364
462.20
80.0
The data in Table 1 thus enables us to produce our Base Case aggregate supply function, which is
presented in Figure 7.
21
22
See Solomon Islands Electricity Authority 2013 Annual Report for details.
Cost, Insurance & Freight.
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
Figure 7:
Aggregate Supply Function – Base Case Scenario
3.3
Plant augmentation options
There are at least three known generating technology alternatives to the existing reciprocating
engines, viz. Hydroelectric, utility-scale Solar PV and Geothermal project proposals. In this
article, we focus on the Hydroelectric and Solar PV options but to be clear, the principles can be
generalised to Geothermal power project options. Our long run cost assumptions are as follows:
Table 2:
Parameter
Unit Fuel Cost
Heat Rate
Capital Cost
Fixed O&M
Variable O&M
Capital Works
Annual Cap Factor
New entrant cost assumptions
Recip Eng. Hydro
Solar PV
$/GJ
28.32
kj/kWh
10,299
$/kW
1,200
5,700
2,500
$/MW/yr 10,000
56,000
30,000
$/MWh
5.00
3.00
1.00
$/MW/yr
2,778
12,500
(%)
25-95
38-45
16-24
Source: ACIL Allen, AGL Energy.
Assumptions underpinning the Hydro have been imputed from public information associated with
the proposed ‘Tina River’ scheme, a nominally 15-20MW planned hydroelectric project with a
potential output of ca.80GWh pa, capital cost of ca.$100 million (or $5000/kW) and annual PPA
charges quoted as ca.$21 million p.a. We have drawn on average monthly rainfall data to
produce our hydrological resource estimates which in turn indicates hydroelectric energy
potential during the wet season (nominally January to March) far exceeds system load, whereas
from May to November, system load exceeds potential hydroelectric energy.23 This is illustrated
in Figure 8.
23
In the absence of more detailed information, we assume the hydroelectric scheme has storage which increases output in dry months
by approximately 3,250 MWh p.a. (i.e. equivalent to 7% of aggregate final demand in during dry months). If our assumption has
under-estimated the Tina River Hydro storage, then project economics and overall power system outcomes will be improved
considerably.
Page 13
Figure 8:
Monthly average rainfall, hydroelectric potential and system demand
In relation to Solar PV generating capacity, we assume an implied (albeit unspecified) mix of
double-axis tracking installations and conventional small-scale roof-top systems. We have
utilised production data from a University of Queensland Solar PV facility, and in particular
hourly production output from March, August, September and October as these have a
reasonable match with various solar irradiance results from Honiara, which has a mean monthly
insolation of 4.88 kWh/m2/day and a low monthly standard deviation of 0.5 kWh/m2/day. Based
on this insolation data, we model annual solar PV capacity factors of 17.2% per annum for any
given level of capacity.
4.
Levelised Cost of Electricity and Power System Models
In this article, we rely on two models which we have formally integrated: (1) a Levelised Cost
Model, and (2) an hourly Power System Simulation Model.
4.1
Levelised Cost Model
The purpose of our Levelised Cost Model is to produce estimates of power plant marginal
running costs and fixed operating & sunk capital costs, the combination of which can be thought
of as generalised estimates of plant long run marginal costs. All costs and prices in the model are
increased annually by a forecast general inflation rate relevant to the Solomon Islands (CPI=3.0)
𝜋 in period (year) z as follows:
𝐶𝑃𝐼 𝑧
𝜋𝑧 = [1 + 100]
(1)
Energy output from each power plant k is a key variable in driving unit fuel costs, variable
Operations & Maintenance costs and the generalised average cost estimates. Energy output is
calculated by reference to installed capacity 𝛾𝑘 , annual capacity factor 𝜎𝑘𝑧 and run time 𝑟𝑡, which
in the Levelised Cost Model is 8,760hrs for each period z. We assume auxiliary losses from onsite electrical loads are trivial and are therefore ignored.
𝜌𝑘𝑧 = 𝛾𝑘 . 𝜎𝑘𝑧 . 𝑟𝑡
(2)
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
To determine the marginal running costs of the kth plant in the zth period, where relevant the
thermal efficiency for each generation plant 𝜁𝑘 needs to be defined. The constant term ‘3600’24 is
divided by the thermal efficiency variable to convert the result from per cent to kJ/kWh, which is
then multiplied by the commodity cost of raw fuel 𝐷𝑘 . In addition to unit fuel costs, Variable
Operations & Maintenance costs 𝑣𝑘 are added. We assume no costs or revenues associated with
climate change policies (e.g. emissions trading schemes, feed-in tariffs and the like). Marginal
running costs in the zth period are then calculated as follows:
𝜗𝑘𝑧 = (
(3600⁄𝜁 )
𝑘
1000
. 𝐷𝑘 + 𝑣𝑘 ) . 𝜋𝑧
(3)
Fixed Operations & Maintenance costs 𝐹𝑂𝑀𝑘𝑧 for each plant k in each period z are calculated by
the product of 𝐹𝐶𝑘 (expressed in $/MW/year) by plant capacity 𝛾𝑘 and escalated accordingly:
𝐹𝑂𝑀𝑘𝑧 = 𝐹𝐶𝑘 . 𝛾𝑘 . 𝜋𝑧
(4)
Capital costs 𝑋𝑘𝑧 for the kth plant in year z for new entrant and sunk plant are expressed as an
overnight capital cost ($/kW) which in turn is a representation of the accumulated annual capital
expenditure program 𝑊𝑘 incurred during the relevant construction period (including interest
during construction) and discounted at the relevant cost of capital 𝑊𝐴𝐶𝐶𝑘 (later defined in eq.7):
−𝑘
𝑋𝑘𝑧 = − ∑𝑁
𝑧=1 𝑊𝑘 . (1 + 𝑊𝐴𝐶𝐶𝑘 )
(5)
Ongoing capital spending for each period z is determined as the inflated annual assumed capital
works program.
𝑥𝑘𝑧 = 𝑤𝑘𝑧 . 𝜋𝑧
(6)
A pre-tax Weighted Average Cost of Capital (WACC) is an essential input to the modelling, and
is defined as follows:
𝑊𝐴𝐶𝐶𝑘 =
𝐸𝑘
𝑉𝑘
∙ 𝐾𝑒 +
Where 𝐸𝑘
𝐷𝑘
𝑉𝑘
𝐾𝑒
𝐾𝑑
𝐷𝑘
. 𝐾𝑑
𝑉𝑘
(7)
= is the value of equity of the kth plant or firm
= is the value of debt of the kth plant or firm
= 𝐸𝑘 + 𝐷𝑘
= the relevant cost of equity capital
= the relevant cost of debt
The general form for computing the Levelised Cost of Electricity for each production technology
can therefore be expressed as follows:
−𝑧
𝑁
−𝑧
𝜃𝑘 = ∑𝑁
𝑧=1[(𝑋𝑘𝑧 + 𝜗𝑘𝑧 ∙ 𝜌𝑘𝑧 + 𝐹𝑂𝑀𝑘𝑧 + 𝑥𝑘𝑧 ). ((1 + 𝑊𝐴𝐶𝐶𝑘 ) )]⁄∑𝑧=1[(𝜌𝑘𝑧 ∙ 𝜋𝑧 ) ∙ (1 + 𝑊𝐴𝐶𝐶𝑘 ) ] (8)
For clarity, deducting Marginal Running Costs 𝜗𝑘 from the generalised Long Run Marginal Cost
Estimate 𝜃𝑘 defines total fixed and sunk capital costs 𝜑𝑘𝑧 , as follows:
𝜑𝑘 = 𝜃𝑘 − 𝜗𝑘
24
(9)
The derivation of the constant term 3600 is: 1 Watt = 1 Joule per second and hence 1 Watt hour = 3600 Joules.
Page 15
These two parameters (i.e. Marginal Running Cost 𝜗𝑘𝑧 and Fixed and Sunk Costs 𝜑𝑘𝑧 ) are key
variables in our hourly power system simulation model, and are used extensively to meet our
overall objective function (see eq.14).
4.2
Power System Model
Our power system model dispatches the fleet of available power generating units to satisfy
differential equilibrium conditions given specified plant options available (outlined in Tables 1
and 2). In the power system model, let P be the ordered set of all hourly periods.
𝑗 ∈ {1 … |𝑃|} ∧ 𝑝𝑗 ∈ 𝑃
(10)
Demand Function
Let L be the set of all electricity consumers in the model.
𝑖 ∈ {1 … |𝐿|} ∧ 𝑙𝑖 ∈ 𝐿
(11)
Let C𝑖 (𝑞) be the valuation that consumer segments are willing to pay for quantity q MWh of
power. We explicitly assume that demand in each period j to be independent of other demand
periods. Let 𝑞𝑖𝑗 be the metered quantity consumed by customer 𝑙𝑖 in each period pj expressed in
MWh.
Supply Function
Let Ψ be the set of existing installed power plants and available augmentation options for each
relevant scenario.
𝑘 ∈ {1 … |𝛹|} ∧ 𝜓𝑘 ∈ 𝛹
(12)
As outlined in eq.9, let 𝜑𝑘 be the fixed operating & sunk capacity costs and 𝜗𝑘 be the marginal
running cost of plant 𝜓𝑘 respectively. Let 𝑃𝑘̇ be the maximum continuous rating of power plant
𝜓𝑘 . Power plants are subject to scheduled and forced outages. F(𝑗, 𝑘) is the availability of plant
𝜓𝑘 in each period 𝑝𝑗 . Annual plant availability is therefore:
∑|𝑃|
𝑗=0 𝐹 (𝑗, 𝑘) ∀𝜓𝑘
(13)
Let 𝑂𝑗𝑘 be the quantity of power produced by plant 𝜓𝑘 in period 𝑝𝑗 .
Objective Function
Optimal welfare will be reached by maximising the sum of producer and consumer surplus, given
by the integral of the aggregate demand curve less power production costs. The objective
function is therefore expressed as:
𝑙
|𝑃|
|𝐿|
|𝑃|
|𝛹|
|𝛹|
𝑖
𝑂𝑏𝑗 = ∑𝑗=1 ∑𝑖=1 ∫𝑞=0
𝐶𝑖 (𝑞)𝑑𝑞 − ∑𝑗=1 ∑𝜓=1(𝑂𝜓𝑘 ∙ 𝜗𝑘 ) − ∑𝜓=1(𝐹𝑂𝑀𝑘𝑧 + 𝑥𝑘𝑧 )
(14)
|𝐿|
|𝛹|
Subject to ∑𝑖=1 𝑞𝑖𝑗 ≤ ∑𝜓=1 𝑂𝜓𝑘 ^ 0 ≤ 𝑂𝑗𝑘 ≤ F(𝑗, 𝑘) ^ 0 ≤ 𝑂𝑗𝑘 ≤ 𝑃̇𝑘
5.
Model Results – Generalised Long Run Marginal Costs
The most recent Annual Report from the Solomon Islands Electricity Authority indicates that
2013 generating fuel costs alone equated to $396/MWh.25 Based on our Levelised Cost Model
25
See SIEA’s 2013 Annual Report for details.
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
and associated assumptions, we estimate total average generation cost of $470/MWh for 2013.
Total average unit cost incorporates a normal level of profit and the much higher cost of remote
and isolated power systems and plants in the Western Provinces. Recall that our analysis focuses
on the Solomon Islands’ main power system. For this, our modelling reveals total average costs
of $379.75/MWh (based on 2014 oil prices).
In our view, the largest risk to maximising consumer welfare in the Solomon Islands is an
inappropriate benchmark. If new projects merely ‘shadow-price’ a suboptimal status quo,
marginal improvements rather than quantum reductions in power system costs may prevail
(colloquially put ‘if you aim for the gutter, you’ll probably hit it’). This is far more than a
theoretical possibility – Figure 9 illustrates average cost curves for Diesel (i.e. reciprocating
engines), Hydroelectric and Solar PV Plant based on Commercial Financing (i.e. without 3PC
policy intervention). The x-axis measures average unit costs (𝜃𝑘 ) and the y-axis measures Annual
Capacity Factor (𝜎𝑘𝑧 ).
Each of the average cost curves in Figure 9 has a marker (i.e. diamond, square, triangle) which
signifies the practical output arising from each generating technology given our resource
estimates.26 Note that Hydroelectric and Solar PV display marginally lower average unit costs
than Diesel.
Figure 9:
Levelised cost results (before 3-Party Covenant Financing)
We have previously highlighted that the cost of capital is a crucially important variable27 and that
it can be favourably impacted by policy intervention. In the analysis which follows, we estimate
the impact of active policy intervention in the form of a 3PC Financing whereby the
Commonwealth Government of Australia wraps long-dated and otherwise un-rated Solomon
Islands Electricity Authority PPAs (i.e. power station revenue stream) for utility-scale
Hydroelectric and Solar PV plant investments. This credit-enhanced PPA enables private sector
project proponents to raise project finance on more favourable rates. It is also likely to materially
reduce the cost of equity capital by removing large components of inherent country and credit
risk.
26
The Diesel marker reflects current utilisation rates of the existing generation fleet given aggregate load.
In the Australian context, Nelson & Simshauser (2013) , Simshauser and Ariyaratnam (2014) and Simshauser (2014) demonstrate
that the WACC is the most critical variable in producing generalised estimates of plant long run marginal costs – reflecting the capitalintensive nature of generating equipment.
27
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For both the cost of equity capital (𝐾𝑒 ) and the cost of debt capital (𝐾𝑑 ) we estimate a 700-800bps
reduction. Figure 10 presents ‘book-end’ funding scenarios with Commercial Finance at one
extreme and 3PC Financing at the other, with a conventional Development Finance scenario in
between (i.e. involving import/export finance or development finance from World Bank, Asian
Development Bank or equivalent institution). While we assume a degree of non-linearity in
relation to the cost of equity capital (𝐾𝑒 ) triggered by the existence of the credit wrap, the primary
variable driving 𝑊𝐴𝐶𝐶𝑘 differentials are the cost of debt (𝐾𝑑 ) and the proportion of debt within
the capital structure (𝐷𝑘 ⁄𝑉𝑘 ).
In producing our first cost of capital scenario, we approached a number of commercial banks
active in the Pacific. At the time of writing, current lending rates for unrated entities were quoted
at 14.5% plus fees and charges resulting in a headline 𝐾𝑑 of 15.75% given 30% gearing levels.
Based on an unrated sovereign and the data contained in Figure 3, we ascribe a 25% cost of
equity capital 𝐾𝑒 . This produces an overall 𝑊𝐴𝐶𝐶𝑘 of 22.2% as illustrated in Figure 10.
In our second scenario involving Development Finance 𝐾𝑑 is estimated to be 9.0% at 50%
gearing and holding 𝐾𝑒 constant at 25% produces a 𝑊𝐴𝐶𝐶𝑘 = 17.0%. Our final scenario presents
a 3PC Financing. With the credit wrap provided by the Australian Government, we calculate a
7.0% cost of debt which incorporates a considerable (although not unreasonable28) credit spread
above cash rates. What has been substantially eliminated is the extent of the country credit risk
premium that would otherwise prevail. We estimate 60% gearing29 (noting that in Rosenberg et
al. 2004 the original 3PC design involved 80% gearing) and 𝐾𝑒 of 18% which produces an overall
𝑊𝐴𝐶𝐶𝑘 of 11.0%:
Figure 10: Weighted Average Cost of Capital (pre-tax)
Figure 11 presents our Levelised Cost estimates for the various technologies given the three
WACC results from Figure 10 (with WACC sensitivities provided in Figures 13-15). The x-axis
measures the unit cost ($/MWh) and the y-axis measures Annual Capacity Factor (ACF, 𝜎𝑘𝑧 ).
Note that following the implementation of a 3PC Financing policy, Solar PV and Hydroelectric
plant now display materially lower average unit cost curves by comparison to status quo Diesel.
28
Our 7.0% cost of debt assumes a spread of 450 basis points. In analysing Solar PV projects in Pakistan, Deutsche Gesellschaft fur
Internationale Zusammernasbeit (2012) recommend using a similar credit spread of 460 bps.
29
This estimate is however it is in line with the analysis undertaken by Doh & Ramamurti (2003) and by Corria da Silva et al. (2006).
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
Deployment of one or both renewable technologies, at scale, is therefore capable of delivering
profound reductions in the wholesale cost of electricity.
Figure 11: Average Cost of Hydroelectric and Solar Plant (WACC scenarios)
Figure 12 unpacks the main cost elements of the three generating technologies at the point of their
respective ‘ACF markers’ to illustrate operational leverage. For Diesel Generation, fixed and
sunk costs (i.e. O&M and Capital Cost elements) amount to just 12% of average total cost and
pale into insignificance by comparison with marginal running costs (the remaining 88% of the
cost structure). In contrast, 3PC-Hydro and 3PC-Solar PV plants are dominated by Fixed & Sunk
Costs. The deployment of these latter technologies will change the operating leverage (and
therefore the cost risk profile) of the entire power system.
Figure 12: Generating technology cost elements
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We noted earlier that our Levelised Cost Estimates are underpinned by a series of estimates with
little in the way of private data, and so understanding the sensitivity of these critical inputs is
important – particularly those relating to our policy target (i.e. WACC). To that end, in Figures
13-15 we present Tornado Charts for each technology to illustrate cost sensitivities to the four
most prominent variables (+/-10%). In each of the following charts the average unit cost has
been identified on the x-axis.
Figure 13: Unit cost sensitivities – Diesel Generation
Figure 14: Unit cost sensitivities – Hydroelectric
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
Figure 15: Unit cost sensitivities – Solar PV
From inspection of Figures 13-15 it is apparent that Diesel Generation is dominated by fuel cost
sensitivities (i.e. the combination of plant Heat Rate and the Unit Fuel Cost of imported liquid
fuels). For Hydroelectric plant, the cost of capital is most important followed by the plant’s
utilisation rate and plant capital cost. For Solar PV, plant capacity factor is the most important
variable, followed by the plant’s capital cost and the cost of capital.
6.
Power System Modelling Results
In our power system modelling, we establish a Base Case and then envisage two alternate
technology scenarios. We start by introducing a hydroelectric plant and then progress by adding
Solar PV capacity. We then model these alternate technology scenarios under two financing
conditions: (1) Commercial Finance, and (2) a 3PC Financing Policy. Accordingly, we compare
and contrast five scenarios in total. We incorporate an own price elasticity of demand of -0.10
and apply this (at the retail tariff level) to system final demand. Relevant System Running Costs
($/yr) and total average cost ($/MWh) for all five scenarios are presented in Figure 16.
In our Base Case, the power system is completely reliant on diesel generators. Given the cost
assumptions in Sections 3 and 5, our model reveals total power system costs of $29.7 million per
annum with fuel costs ($23.6 million or 80%) dominating the cost structure. As Figure 16
reveals, this translates into an average unit cost of $379.75/MWh. In our view, a peak tariff of
$495.57/MWh and an off-peak tariff of $309.10/MWh would represent an economically efficient
wholesale pricing structure (see Appendix I for calculations).
Page 21
Figure 16: Power system modelling results
In the ‘15MW Hydro Scenario’ (without 3PC Financing) our power system model optimises
installed capacity given our hydrological resource estimates, a constraint of 3 units (with perfect
capacity divisibility) and a total capacity constraint of 15-20MW. The cost of capital has a
material effect on the optimisation. Without 3PC Financing, our power system model finds
15MW to be optimal – that is, at the bottom of the 15-20MW constraint.30 The 15MW
hydroelectric scheme was found to produce 55,864,000 kWh (i.e. 95.8% of wet season aggregate
demand, and 55.6% of dry season aggregate demand) and requires annual PPA payments of $18
million. This scenario therefore has a total average cost of $343.28/MWh representing a
$36.47/MWh (-9.6%) improvement over the Base Case.
In the second alternate scenario, we consider the 15MW Hydro + Solar PV. Once again the cost
of capital has a material effect on the optimisation. Our model finds the addition of 2.5MW of
Solar PV capacity to be optimal. This in turn reduces average system costs to $326.11/MWh – a
cumulative reduction of $53.46/MWh (-14.1%).
Recall from Figure 11 that substantial gains in welfare could be achieved under 3PC Financing
arrangements. In our third alternate scenario involving 3PC Financing, our model finds an
expanded 18 MW hydro plant to be the optimal size (i.e. 3 x 6 MW units). This involves a capital
investment commitment of $102.6 million including $61.5 million of project debt. 3PC
Financing has the effect of reducing annual PPA costs from an equivalent $21.1 million pa (i.e.
for 18MW of capacity) to just $10.0 million pa. The larger 18MW hydroelectric plant produces
62,863,000 kWh or 79% of aggregate final demand (i.e. virtually 100% of system electrical load
during the wet season and 66.3% of final demand in the dry season). In this 3PC Hydro Scenario,
total system costs reduce to $16.3 million per annum – an average unit cost of $204.24/MWh.
Annual diesel fuel costs have been reduced from $23.6 million in the Base Case to $4.9 million
p.a. The 18MW plant also enables 12.3MW of existing Diesel Generators to be redeployed (or
decommissioned).
In our final scenario involving Hydro + Solar PV capacity under 3PC Financing, our power
system model commences with the 18 MW hydroelectric capacity as committed and then
If the ‘15MW minimum capacity’ constraint is relaxed, pure cost minimisation suggests only 11MW of capacity be built given our
hydrological assumptions and perfect divisibility of unit capacity.
30
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AGL Applied Economic and Policy Research
optimises the installed capacity of Solar PV plant. To be sure, we relax any spatial constraints
that would otherwise limit installed Solar PV capacity in order to identify an ‘economic saturation
point’. With 3PC Financing, our model finds that any addition of Solar PV capacity of up to
12MW (compared to 2.5MW without 3PC) has the effect of reducing overall system running
costs and enhancing welfare. This involves further capital investments of up to $30.0 million.
Figure 16 illustrates that system running costs in this scenario reduce to $15.2 million p.a. or
$190.84/MWh with diesel fuel costs falling to just $1.1 million p.a. Note that by comparison to
the Base Case, system running costs have been virtually cut in half, contracting by 49.8% on an
expanded system final demand of 79,714,000 kWh (up 1,540,000 kWh or 2.0%).
In this final scenario, the addition of the 18MW hydro plant and 12MW Solar PV capacity
enables 17.7MW (78%) of Diesel Generation to be redeployed (or decommissioned). Above all,
power system reliability has been improved considerably. Our model produced lost load of
0.022% in the Base Case, 0.004% lost load in the 3PC-Hydro Scenario, and no lost load in our
3PC-Hydro + Solar Scenario. Furthermore, while not a focus of our research, greenhouse gas
emissions have been reduced by 95.2%. In summary, under our 3PC Scenario, welfare is
unambiguously improved.
To be sure, regardless of the financing arrangements and optimised plant configurations, some
level of diesel plant capacity is necessary to ensure the reliability of supply. In our power system
modelling, we essentially treat Solar PV output as first in the merit order followed by hydro
capacity. We then optimise the run-time of any requisite diesel plant (at greatly reduced loads
and output levels) in order to maintain security of supply. How this is achieved is that in dry
months when Hydro and Solar output are insufficient to satisfy 100% of aggregate final demand,
Diesel Generator fuel costs are minimised by targeting constant run times (i.e. to maximise
thermal efficiency). Hydroelectric plant thus supplies residual semi-base and peak loads. Figures
17 and 18 illustrate this by presenting an ‘average daily dispatch’ of plant during a typical wet
season day and a typical dry season day.
Figure 17: Typical daily dispatch – wet season
Page 23
Figure 18: Typical daily dispatch – dry season
7.
Policy implications and concluding remarks
In this article, we presented a 3PC Financing policy designed to substantially reduce the
production costs associated with capital-intensive power projects in an unrated sovereign nation.
Such a policy and associated prescriptions are not specific to the Solomon Islands, and the
conceptual framework and associated financial logic that underpins the initiative can be
generalised to other ‘user pays’ infrastructure projects, and, to other developing nations. The
broad applicability of 3PC financing means that it is not country specific, project specific or asset
class specific.
We focused on the Guadalcanal power system which supplies the commercial hub, Honiara. By
investing in Hydroelectric and Solar PV plant capacity (or other renewable technologies such as
geothermal plant), the wholesale cost of electricity can be reduced. The question we have
attempted to answer is, with policy intervention, by how much? Our Base Case scenario
commenced with an average wholesale cost of $379/MWh. When we added Hydro + Solar PV
capacity without policy intervention, a modest cost reduction was achieved ($326/MWh, down
14.1%). 3PC policy intervention cut electricity production costs in half ($190/MWh, down
49.8%). While not a primary driver of our modelling, 3PC Financing also produced a -95.2%
reduction in Solomon Islands greenhouse gas emissions from electricity generation. Our
modelling results indicate unambiguous improvements in consumer welfare and environmental
outcomes.
To be clear, a range of non-trivial issues require resolution before a 3PC Financing policy could
be contemplated in the Solomon Islands. Land access and detailed modelling (using high quality
data) are clearly essential pre-conditions. But resolving property rights issues (i.e. in relation to
land as security for finance) would appear to be a threshold issue of paramount importance to
development agendas.
Financing improvements in the Solomon Islands power system have historically been sourced
from the World Bank , the Asia Sustainable and Alternative Energy Program trust funds, the
International Finance Corporation, the Australian Agency for International Development
(AusAID) and other foreign governments. Clearly, these sources of finance will remain essential
for improvements in the overall operation of power systems and the electrification of rural areas.
Working Paper No.47 – Solomon Is
AGL Applied Economic and Policy Research
For rural and remote areas, small-scale solar PV and battery represent a promising prospect. But
for the utility-scale projects that we examined, 3PC Financing was capable of producing quantum
reductions in system operating costs.
We expect 3PC policy to have the effect of encouraging more private sector involvement in, and
competition for, investment in strategic infrastructure projects. Further, the involvement of
project banks should ensure the standards of technical due diligence that typically occurs in
Australian power projects also occurs in the Solomon Islands. Under these conditions, the capital
stock should continue to accumulate rather than risk the excess depreciation scenario we
identified earlier through Solow’s (1956) theory of growth and development.
By leveraging balance sheets, donor governments may continue foreign aid programs and modify
the fiscal mix to suit their own macro conditions while simultaneously improving the welfare and
standard of living of the aid-recipient country population – in this instance Solomon Islanders.
While 3PC Financing may appear novel, the concept is not without broader precedent in
Australia. The Clean Energy Finance Corporation (CEFC) does exactly this for renewable
projects in Australia. 3PC Financing appears to be consistent with the basic tenets of the
Australian Government’s ‘new aid paradigm’ (see Bishop, 2014). 3PC Financing would thus
appear worthy of consideration by Australian policymakers. If this is the case, the question for
policymakers is how to translate our desktop modelling results into practice.
Ordinarily in power system planning the cost of supply, reliability of supply and environmental
objectives collide and require continuous trade-off by policymakers. It must be rare in practice to
see these trade-offs avoided. Yet the Solomon Islands appears to be a unique case in point. The
opportunity exists to dramatically reduce the cost of energy supply, increase the security of
supply, and improve the environmental performance of electricity production through a 3PC
Financing policy. The policy implications of our research are therefore relatively
straightforward: by utilising a credit-wrap and improving the credit quality of a project income
stream, the cost of large capital-intensive user-pays infrastructure projects can be meaningfully
reduced. Given the relationship between the availability and affordability of electricity and
human development, devoting effort to such a policy appears warranted.
8.
Declaration of the Authors
This working paper was produced as a component of AGL Energy Ltd’s ‘Energy for Life’
corporate citizenship program, which encourages and facilitates employees to contribute to the
communities in which we serve by volunteering. Like all good corporate citizenship programs,
one of the key elements is to encourage employees to utilise their skills when volunteering. As
energy economists, the authors chose to use some of their ‘downtime’ during the Christmas/New
Year period to complete the analysis contained within this Working Paper.
AGL Energy Ltd has no commercial interest in the Solomon Islands. The authors were inspired
by one of AGL’s charity partners, CARE Australia. CARE is an international humanitarian aid
organisation fighting global poverty, with a special focus on working with women and girls to
bring lasting change to their communities. The analysis contained within this Working Paper was
intended to provide policymakers with insights and perspectives on how to increase
electrification, radically reduce the cost of energy and in turn lower the incidence of energy
poverty in a developing economy.
An earlier draft of this Working Paper was kindly reviewed by Dr the Hon. Craig Emerson,
Professor Stephen Gray (The University of Queensland), Vijendra Satkunasingam (UBS
Investment Bank), Shobana Venkataraman (International Finance Corporation), Erik Caldwell
Johnson (World Bank), James Nelson (PwC Debt & Capital Advisory), Anna Stewart (ANZ
Page 25
Bank) and Keith Orchison. However, all remaining errors and omissions are entirely the
responsibility of the authors.
9.
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Working Paper No.47 – Solomon Is
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Appendix I: Bulk Supply Tariff Structure in the Base Case
Following on from eq.10-14, let Ix and Iy be ‘peak’ and ‘off peak’ periods respectively, with
peak defined as 9pm to 6pm on weekdays with all other periods defined as ‘off-peak’. Let Tx and
Ty be the ‘peak’ and ‘off peak’ tariffs respectively (expressed in $/MWh), and let Q x and Q y be
aggregate quantity consumed during ‘peak’ and ‘off peak’ periods respectively (measured in
MWh). Therefore:
𝑃 = 𝐼𝑥 ∪ 𝐼𝑦
∑|𝐿|
𝑖=1 𝑞𝑖𝑗 = 𝑄𝑥 + 𝑄𝑥
∀𝑥, 𝑦|𝑥 ≠ 𝑦 ^ 𝐼𝑥 ∩ 𝐼𝑦 = {} ^𝑄𝑥 ∩ 𝑄𝑦 = {}
Recall that 𝜑𝑘 = 𝜃𝑘 − 𝜗𝑘 . Therefore,
|𝐿|
𝑇𝑥 = 𝜗𝑘 +
𝜑𝑘 ∙∑𝑖=1 𝑞𝑖𝑗
𝑄𝑥
|𝐿|
^ 𝑇𝑦 = 𝜗𝑘 | (𝑇𝑥 ∙ 𝑄𝑥 + 𝑇𝑦 ∙ 𝑄𝑦 ) ≡ (𝜃𝑘 ∙ ∑𝑖=1 𝑞𝑖𝑗 )
Given these parameters we would structure a bulk supply tariff as follows:

Peak Tariff 𝑇𝑥 = $495.57, and

Off-peak Tariff 𝑇𝑦 = $309.10.
Page 31