Policies to abate carbon emissions have a range of economic impacts. Of central concern to governments has been the impact of such policies on the price of electricity. Fast rising electricity prices are politically damaging because they tend to affect low income groups disproportionately but rising electricity price also provides an incentive to reduce carbon emissions. Australia is an interesting case study in this regard, having introduced a relatively high $23/tC[O.sub.2] carbon price in 2012. Prior to the introduction of this policy, compensation was provided to low- and middle-income groups to cover the anticipated transmission in price rises from wholesale to retail prices. But what exactly is the effect of a carbon price on wholesale electricity prices? This paper provides an answer to this question.
Understanding of and estimation of the carbon pass-through rate is essential to estimating the interaction between the carbon price and electricity prices and assessing the need, scope and role of industry assistance, including partial or complete allocation of free permits (e.g. 'grandfath-ering') (Reinaud 2007, Chen et al. 2008, Chernyavs'ka and Gulli 2008 , Freebairn 2008, Sijm et al. 2008, Simshauser 2008, Menezes et al. 2009, Simshauser and Doan 2009, Kim et al. 2010, Nelson et al. 2010, Sijm et al. 2012).
This article examines carbon pass-through in Australia but this issue is of interest in many other countries, as many countries or states have or are planning to adopt carbon pricing. The Kyoto Protocol allows flexibility over mechanisms used by signatory countries to meet their emission targets. For instance, emission trading allows countries that exceed emission reduction targets to sell excess greenhouse gas permits to deficit countries, which links Emission Trading Schemes (ETSs) into an international market (Parliament of Australia 2013). Currently, there are several ETSs operating including the European Union (comprising 31 countries), Switzerland, New Zealand, Australia, Japan and Kazakhstan. Significant state based schemes include: USA Regional Greenhouse Gas Initiative (nine eastern states including New York and Massachusetts); Western Climate Initiative (five USA and Canadian states including California, Quebec and British Columbia); Japan (Metropolitan scheme in Tokyo and provincial scheme in Saitama Prefecture); and Canada (Alberta). Proposed schemes include: China (seven provinces and cities including Beijing and Shanghai); Republic of Korea, Belarus, Brazil, India, and Mexico (Sterk and Mersmann 2011, Climate Commission 2013, DIICCSRTE 2013, Evans et al. 2013, Parliament of Australia 2013).
We use an agent-based model of the NEM called the Australian National Electricity Market (ANEM) model to estimate the carbon pass-through rate, so evaluating the relationship between carbon prices and wholesale electricity prices. ANEM's methodology assumes an Independent System Operator (ISO) and uses Locational Marginal Pricing (LMP) to price energy by the location of its injection into, or withdrawal from, the transmission grid. ANEM is based on the American Agent-Based Modelling of Electricity Systems (AMES) model (Sun and Tesfatsion 2007a, 2007b). The ANEM model fully reflects the differences between the institutional structures of the Australian and USA wholesale electricity markets. (1)
We consider that the fuel-mix of the market will be of greater importance in ultimately determining carbon pass-through rates while acknowledging wholesale market structure could affect pass-through estimates, such as the transparency and bidding behaviour in day-ahead and balancing markets of a net pool market as in the USA. However, the gross pool market structure of the NEM provides advantage in estimating carbon pass-through rates because this market structure most closely matches the framework underpinning discussion of carbon pass-through in the broader literature.
The wholesale market of the NEM is a real time, 'energy only' market and a separate market exists for ancillary services (AEMO 2010). The ANEM model uses a DC OPF algorithm to determine optimal dispatch of generation plant, power flows on transmission branches and wholesale prices. The ANEM model accommodates: intra-state and inter-state power flows; regional location of generators and load centres; demand bid information; accommodation of unit commitment features including variable generation costs, thermal limits, ramping constraints, start-up costs and minimum stable operating levels.
The next section examines carbon pass-through, the impact of carbon prices on wholesale electricity prices and claims made for industry assistance. Section 3 provides an outline of the ANEM model. Section 4 discusses implementation issues of the ANEM model. Sections 5 and 6, respectively, analyse the sensitivity of the wholesale price and carbon pass-through rate to carbon price. Section 7 discusses policy implications and Section 8 offers conclusions.
CONCEPT OF CARBON PASS-THROUGH
Carbon pass-through can be defined as the incidence of a fixed carbon price or tradable carbon permit and refers to the proportion of carbon price (expressed in $/tC[O.sub.2]) that is passed into wholesale electricity spot prices (expressed in $/MWh) (Nelson et al. 2010). The carbon pass-through rate is influenced by:
* Emissions intensity of the existing capital stock (Simshauser and Doan 2009, Kim et al. 2010, Nelson et al. 2010).
* Demand and Supply elasticities (Chen et al. 2008, Freebairn 2008, Menezes et al. 2009, Nelson et al. 2010, Sijm et al. 2012).
* Economics of existing substitutes allowing a switch from high to low carbon emission forms of generation (Simshauser and Doan 2009, Nelson et al. 2010).
* Availability of offsets or international credits (Nelson et al. 2010).
* Market competition, e.g. whether the market is competitive or characterised by oligopolistic or monopolistic structures (Chernyavs'ka and Gulli 2008, Nelson et al. 2010, Sijm et al. 2012).
Most carbon pass-through rate calculations make simplifying assumptions such as perfect competition and ignore transmission branch congestion and the spatial location of generators and demand centres within the transmission grid. Such calculations fail to consider market power and constraints other than generator capacity limits and least cost production. Least cost production involves dispatching the generator with lowest marginal cost first, followed by the generator with the next lowest marginal cost, and so on, (Sijm et al. 2006, Chen et al. 2008, Sijm et al. 2012). However, 'out of order' dispatch can arise under the following circumstances: when market power is exercised; account is taken of transmission and unit commitment features; the level of the carbon price changes the merit order and marginal generator; or the carbon price produces a demand response (Sijm et al. 2006, Chen et al. 2008).
Carbon price investigations in Australia fall within two broad categories: economy-wide and specific electricity industry studies. Economy-wide studies typically utilise Competitive General Equilibrium (CGE) modelling where full carbon pass-through is assumed (Allen Consulting 2006, Garnaut (2008, 2011a), Prime Ministerial Taskforce 2008, Department of Treasury 2011). Specific electricity industry studies usually model the wholesale electricity market using linear programming (MMA 2006, ROAM (2008, 2011), SKM-MMA 2011, ACIL Tasman 2012).
Many of these studies investigate carbon pass-through indirectly where wholesale electricity price are presented relative to a 'Business-As-Usual' (BAU) benchmark without explicitly calculating the carbon pass-through rate (MMA 2006, NETT 2006, ROAM 2008, Garnaut 2011b). Later reports have been more likely to explicitly calculate the carbon pass-through rate, which reflects a growing concern over wholesale electricity price increases induced by a carbon price (Department of Treasury 2011, ACIL Tasman 2012).
Nelson et al. (2010) in a survey of Australian carbon pass-through rates find that state emission factors measured in (t[C0.sub.2]/MWh), including the contribution of wind generation, produced a variable set of outcomes with Victoria (VIC) having the largest emissions intensity factor of 1.23 while Tasmania (TAS) had the lowest of 0.32. The emissions intensity factors for Queensland (QLD) and New South Wales (NSW) were 0.89 and 0.90 respectively while for South Australia (SA) it was 0.72, which reflects SA's higher concentration of wind generation. The NEM wide weighted average emissions intensity factor was 0.94.
Nelson et al. (2010) also demonstrate that Australian estimates of carbon pass-through varied significantly from 17% to 128% with a mean of 93.4% for stable generator bidding strategies. When capital stock 'fixity' is assumed, higher range values are found (Freebairn 2008). Department of Treasury (2011, p. 126) cite SKM-MMA (2011) and ROAM (2011) who estimate an aggregate carbon pass-through rate of 0.85. ACIL Tasman (2012, p. 27) estimate State carbon pass-through rates of: 0.83 (QLD), 0.91 (NSW), 0.68 (VIC), 0.63 (SA) and 0.48 (TAS). VIC's low pass-through rate results from competition with SA's low emission intensive gas and wind generation and with NSW's and TAS's hydro generation (ACIL Tasman 2012, p. 47).
Investigations of the EU ETS indicate that carbon pass-through rates are broadly correlated with average emission intensity levels. Reinaud (2007), Sijm et al. (2008), Kim et al. (2010) and Nelson et al. (2010) provide an overview of methods and carbon pass-through estimates.
In principle, two types of carbon pass-through rates have been identified in the literature: add-on and work-on. The 'add-on' pass-through rates are the carbon intensity rates of generators (Sijm et al. 2006). In comparison, the 'work-on' pass-through rates indicate how much of the carbon price is passed onto wholesale electricity prices. This work-on rate is...
Impact of Carbon Prices on Wholesale Electricity Prices and Carbon Pass-Through Rates in the Australian National Electricity Market.
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