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Impact of high variable renewable generation on future market prices and generator revenue


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This study assesses the potential impact of high renewable generation on the spot electricity prices, generator revenue and profits in an energy-only electricity market. In particular, it presents modelling outcomes for the Australian National Electricity Market (NEM) with a range of possible renewable penetrations in 2030. It is assumed that the current reliability standard is maintained and participants deploy short run marginal cost bidding. The study found that increasing the share of wind and PV generation would likely result in lower average spot prices and subsequently revenue and profit of generators. The revenue impact on large-scale PV was found to be very severe and could lead to insufficient revenue to cover the costs, particularly at higher renewable penetrations. Changes in market mechanisms, such as increasing the Market Price, may be required to ensure revenue sufficiency and long-term resource adequacy in an energy-only market with high renewables.
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AbstractThis study assesses the potential impact of high
renewable generation on the spot electricity prices, generator
revenue and profits in an energy-only electricity market. In
particular, it presents modelling outcomes for the Australian
National Electricity Market (NEM) with a range of possible
renewable penetrations in 2030. It is assumed that the current
reliability standard is maintained and participants deploy short
run marginal cost bidding. The study found that increasing the
share of wind and PV generation would likely result in lower
average spot prices and subsequently revenue and profit of
generators. The revenue impact on large-scale PV was found to
be very severe and could lead to insufficient revenue to cover the
costs, particularly at higher renewable penetrations. Changes in
market mechanisms, such as increasing the Market Price, may be
required to ensure revenue sufficiency and long-term resource
adequacy in an energy-only market with high renewables.
Index Terms Revenue sufficiency, energy only market,
renewable, Australian National Electricity Market (NEM)
ENEWABLE generation sources, particularly wind and
solar photovoltaics (PV), are fast becoming major
generation sources in a number of electricity industries. This is
due to falling solar and wind energy technology costs and
growing concerns over climate change and energy security.
Due to the variable availability and somewhat unpredictable
nature of wind and PV generation, there are concerns over the
potential impacts of such renewable sources on the electricity
industry. For restructured electricity industries with
competitive market arrangements, the high capital yet low
operating costs (short run marginal cost or SRMC) of these
technologies poses some interesting additional challenges. In
particular, growing penetrations of low SRMC renewable
generation in energy-only wholesale markets are likely to
reduce spot electricity prices and hence market returns to all
The risk of insufficient revenue to recover both fixed and
variable operation costs is one of the major concerns for
generators. Concerns over revenue sufficiency are also shared
by many policy makers and market regulators given that this
might lead to long-term resource adequacy challenges by
promoting early retirement and deferred entry to the market,
which can reduce the reliability of the electricity supply [1, 2].
This work has been supported in part by Australian Renewable Energy
Agency (ARENA) and the CSIRO Future Grid Project.
The authors are with the Centre for Energy and Environmental Markets
and School of Electrical Engineering and Telecommunications, UNSW
Australia, Sydney, Australia (email:
The Australian National Electricity Market (NEM) is a
moderately sized market (around 35GW of peak demand and
200TWh per year) with growing wind and solar deployment
and significant renewable resource potential, It features a
relatively transparent energy-only market with relatively few
constraints imposed on generation offers, and therefore
provides an interesting case study for analysis of high
renewable scenarios, and their revenue implications.
Previous studies have explored the technical feasibility and
economics of high renewable scenarios in the NEM, including
scenarios of 100% renewable energy [3, 4]. However, these
studies have not directly quantified the revenue implications of
these high renewable systems. Some observers have raised
questions about the feasibility of the NEM’s energy-only
market design in high renewable scenarios, including claims
that a system composed of a majority of low SRMC
generation may not deliver appropriate commercial incentives
for assured resource adequacy [5].
This study aims to examine the possible impact of high
renewable penetrations on spot electricity prices, generator
revenues and profits in a future Australian NEM in 2030, with
a view to assessing the potential viability of the present
energy-only market and its mechanisms to ensure resource
adequacy and hence long-term reliability. The paper provides
quantitative analysis using a long-term generation portfolio
planning and investment modelling tool first developed in [6].
A number of high renewable penetrations are considered
including uncertainties associated with these. The modelling
assumes that the current NEM reliability standard is
maintained and participants deploy SRMC bidding.
This study uses a probabilistic generation portfolio
modelling tool which extends the commonly applied load
duration curve (LDC) based optimal generation mixes by
using Monte Carlo simulation to incorporate key uncertainties
into the assessment [6]. These uncertainties include future gas
costs, carbon policies and electricity demands. The tool
determines a probability distribution of annual revenue,
operating costs and profits/losses of each generation
technology for different possible generation portfolios. The
“expected” annual revenue, operating cost and profit of each
generation technology for a particular portfolio represent the
average of all the simulated revenue, costs and profits from
every Monte Carlo run. Generators obtain revenue through a
spot market based upon the spot electricity price (or “market
clearing price”) in each period.
Impact of High Variable Renewable Generation
on Future Market Prices and Generator Revenue
P. Vithayasrichareon, Member, IEEE, J. Riesz, Member, IEEE and I. MacGill, Member, IEEE
Generators are dispatched based on their SRMC with the
objective of minimizing the total system operating cost of
meeting demand in a year subject to demand balancing
constraints. SRMC is the sum of the fuel, variable operations
and maintenance (O&M) and greenhouse emissions costs of
each unit. The modelling assumes that generators bid into the
market at their SRMCs and the spot price is the cost to supply
the last MW of electricity to meet demand.
PV and wind generation is incorporated into the modelling
through the use of a residual (net) load duration curve (RLDC)
approach to capture the chronology of PV and wind resource
variability and its match to NEM electricity demand, based
upon historical correlations observed in 2010. As the lowest
SRMC generation, PV and wind generation is dispatched first
in the merit order. With this approach, hourly simulated PV
and wind generation is subtracted from hourly demand over
the year to obtain residual demand, which is then rearranged to
obtain a RLDC. It is this curve which has to be met by
conventional technologies in the portfolio.
A 15% minimum synchronous generation requirement is
applied in all dispatch periods to provide adequate system
inertia, fault feed-in levels and system stability [7]. This
represents the minimum amount to which aggregate
conventional generators can be turned down. This constraint is
important for high renewable scenarios since some of the most
promising kinds of renewable generation (notably wind and
PV) are non-synchronous and therefore do not generally
provide inertia and fault feed-in current to the system [1]. For
the purposes of this study, coal, gas and hydro plants are
assumed to provide synchronous generation (some types of
renewable generation such as solar thermal, geothermal and
biomass are also synchronous, although these technology
types have not been modelled in this study).
In addition to the market revenue, conventional generators
also receive a supplementary payment in periods in which they
are dispatched out of merit order to satisfy the 15%
synchronous requirement constraint. This supplementary
payment is referred to in this study as a “constrained on
payment”, and is determined based upon the SRMC of the
most expensive conventional generator that is dispatched to
meet the synchronous requirement.
For each Monte Carlo run, annual revenue, of each
generation technology is calculated according to Eq. (1) (3).
Spot market revenue
1t tt,nn MCPREVSpot
Constrained on payment
1t t,nn maxSRMCPconvCP
Annual revenue REVTotaln = REVSpotn + CPn (3)
where Pn,t is the generation output of technology n (MW), MCt
is the market clearing price ($/MWh), Pconvn,t is the output of
conventional generator (MW) that is dispatched out of merit
order to meet the synchronous requirement, SRMCmaxt is the
SRMC of the most expensive generator ($/MWh) that is
dispatched to meet the synchronous requirement in period t.
Annual operating cost and profit of the generator are
determined based upon Eq. (4) (5) respectively.
1t t,nt,nn SRMCPOPEX
OPProfitn =
where SRMCn,t is the SRMC ($/MWh) of technology n in
period t and FOMn is the annual fixed operating and
maintenance (O&M) costs ($) of technology n.
Six different renewable penetration scenarios for the NEM
in 2030 were considered: 15%, 30%, 40%, 60%, 75% and
85% (by energy contribution). Eight technologies were
included: coal, combined cycle gas turbine (CCGT), open
cycle gas turbine (OCGT), co-generation, distillate, utility-
scale PV (single axis tracking), wind (on shore) and hydro.
The renewable penetration scenarios and the percentage of
each renewable technology are summarised in TABLE I. Note
that the proportion of PV and wind energy for each renewable
penetration were selected based on assumptions on future
investment scenarios, as explained in [8].
Achieved total
% PV
% Wind
% Hydro
% Fossil
The maximum spot price is set at $13,500/MWh, which is
the current Market Price Cap (MPC) for the NEM [9]. This
price is triggered in periods when demand exceeds available
generation capacity. The installed capacity was determined so
that each generation portfolio will, on average, meet the
present NEM reliability standard of 0.002% annual unserved
energy (USE).
A. Hourly Demand and Generation
An hourly electricity demand profile for 2029-2030 was
obtained from analysis by the Australian Energy Market
Operator (AEMO) on a 100% renewables system under a
moderate economic growth scenario. Hourly wind and solar
output profiles for 2030 were simulated from hourly traces of
1-MW on-shore wind and solar PV (single axis tracking)
generation in different locations across the NEM provided by
AEMO [7]. For hydro generation an annual hydro energy
dispatch limit of 13 TWh was applied, based upon the long-
term average hydro generation estimated by AEMO [7].
Generation output of each thermal technology (coal,
CCGT, OCGT, cogen and distillate) in each period was
determined using merit order dispatch based upon their
SRMCs in 2030. Technical and cost parameters of generating
plants were based upon a previous study presented in [8].
B. Modelling Uncertainties
Key uncertain parameters considered in the modelling are
gas prices, carbon prices and electricity demand as they have
experienced a higher degree of uncertainty than other variables
[10, 11]. Lognormal distributions were applied to model future
fuel and carbon prices to reflect the asymmetric downside risk
associated with high price outcomes. Demand uncertainty was
modelled assuming a normal distribution of residual peak
demand for each renewable penetration scenario. Both
lognormal and normal distributions can be characterized by
their mean (expected value) and standard deviation (SD).
The mean and SD of fuel prices and carbon prices were
determined based upon Australian Government estimates for
2030 [12, 13]. Correlated samples of coal, gas and carbon
prices were simulated from their marginal lognormal
distributions 10,000 times using Multivariate Monte Carlo
simulation techniques described in [6]. The mean and SD of
peak demand were estimated based on the Probability of
Exceedance (POE) demand projections in 2029-2030 provided
in [7], and were explained in detail in [8]. Residual peak
demand were also simulated 10,000 times. In order to achieve
the 0.002% USE reliability standard on average, there were
instances where the simulated residual peak demands
exceeded the installed fossil-fuel generation capacity.
With Monte Carlo simulation techniques, the modelling
calculated overall generation costs, emissions, revenue and
operating profits for each technology within each possible
generation portfolio for 10,000 simulated future fuel prices,
carbon prices and electricity demands. The cost of USE is
valued at the MPC ($13,500/MWh), and is included in the
overall generation cost.
Fig. 1 illustrates the efficient frontiers consisting of optimal
generation portfolios in terms of expected generation cost and
cost risk (SD of cost) for different generating portfolios,
ranging from 15% to 85% renewable generation. Each dot is a
plot of a portfolio’s expected costs (against the vertical axis)
and the cost risk (against the horizontal axis), calculated over
10,000 simulations.
As illustrated in Fig. 1, the lowest cost
generation portfolio features 60% renewable energy, with an
expected cost of $92/MWh. The costs rise as renewable
energy increases to 75% and 85%, and are also higher for the
renewable penetration levels below 60%.
For the purpose of this discussion, only the revenue and
profits of each technology in the least cost portfolio for
different renewable penetrations are quoted. For example, the
least cost portfolio for the 15% renewable portfolio is the one
that consists of 41% coal, 21% CCGT, 7% OCGT, and this
lowest cost portfolio is used as the basis for analysis.
The average spot price duration curve for the least cost
portfolio in each renewable penetration for the highest 2%
price periods is shown in Fig. 2. The figure also shows the
corresponding PV and wind generation outputs in those
periods. The results suggest that the magnitude of price spikes
For each renewable penetration, the amount of distillate, cogeneration,
hydro, PV and wind capacity was fixed for every possible thermal portfolio.
increases with higher renewable penetrations but the high
price periods (e.g. greater than $500/MWh) are less frequent.
For example, the average highest spot price in the 85%
renewables scenario is around $8,500/MWh compared to
$1,500/MWh in the 15% renewables scenario. However the
number of periods where the spot prices are greater than
$500/MWh is less than 0.4% of the time (35 hours per year) in
the 85% renewables scenario compared to 2% of the time (75
hours per year) in the 15% renewables scenario.
Fig. 1. Efficient frontiers containing optimal generation portfolios for different
renewable penetrations in 2030. The capacity of fossil-fuel technologies in
each portfolio is shown in GW (in brackets) and percentage share. The
coloured boxes show the share of each technology by capacity installed.
Fig. 2. Average market price duration curve for the top 2% of the price
periods and the corresponding PV and wind generation.
Since the model does not incorporate strategic bidding
behaviour, high spot prices in the model are driven by periods
where unserved energy is occurring. This means that the price
duration curves illustrate that there are fewer periods of supply
and demand imbalance as the renewable penetration increases.
However, the magnitude of USE occurring in each of those
periods is higher (USE is concentrated into fewer periods as
Note that the figure shows the ‘average’ spot price across 10,000
simulated fuel prices, carbon price and electricity demand for each period.
Without modelling the uncertainties, the highest spot price shown on the
graph would be $13,500/MWh.
the renewable percentage increases, keeping in mind that the
total USE for each generation portfolio is the same).
Fig. 3 shows the expected annual generator revenue and
operating profit of each technology in the least cost portfolio
for each renewable penetration. The annual average spot
prices are also shown in Fig. 3(a). The impact of the carbon
price on the revenue, operating costs and hence profits of the
fossil fuel plants are apparent.
Although the revenues of PV and wind plants are relatively
low, their operating profits of PV and wind plants are
significantly higher than those of coal and CCGT, particularly
at low to moderate renewable penetrations (i.e. from 15% to
60% renewable penetration). This is due the low operating
cost of renewable generation and the impact of carbon price on
the high operating costs of fossil fuel plants.
The operating profit of each technology generally reduces
as the amount of renewables increases due to lower annual
average spot prices influenced by the low SRMCs of wind and
PV. However, fossil fuel generators are able to make operating
profit even at high renewable penetration. This is likely
influenced by the 15% minimum synchronous generation
requirement applied in the modelling, which enforced thermal
generating plants (most likely coal) to supply at least 15% of
demand in every period. Hence they are able to earn revenue
to most periods. This is particularly crucial during scarcity or
near scarcity periods when the spot prices are extremely high.
Fig. 3. (a) Expected annual revenue of each technology and annual average
spot prices (b) Expected annual operating profit of each technology for each
renewable penetration. The capacity (MW) of each technology is also shown.
On the other hand, the profits of PV and wind reduce far
more significantly than for thermal generation technologies.
This is particularly the case for PV as shown by its negligible
operating profit at an 85% renewable penetration even without
taking into account annual capital repayments. Since PV, and
to a lesser extent wind, do not often generate during high price
periods (as shown in Fig. 2), they were unable to benefit from
the high spot prices. This result may due to the very large
proportion of PV included in the 75% and 85% renewable
portfolios, which may be higher that economically optimal,
resulting in high costs and almost negligible profits. These
issues warrant further investigation
The modelling results suggest that, at high renewable
penetration levels and given the current market arrangements,
PV and wind plants might not earn sufficient revenue to cover
their costs. In contrast, coal and CCGT plant appear to
maintain operating profitability following an initial decline.
OCGT plant appear to maintain operating profitability
regardless of the renewable penetration level, suggesting that
peaking plant may be relatively immune to the reducing
average wholesale price, and able to flexibly adjust as required
to access high priced periods. One of the options for
increasing generator revenue is to increase the MPC from the
current $13,500/MWh, since a higher MPC will lead to more
revenue earned during high demand periods and hence higher
profits for generators [14]. This will be examined in future
This paper assesses the impact of variable renewable
generation on spot market prices and generator revenues in an
energy-only electricity market. The Australian National
Electricity Market (NEM) with different renewable
penetrations in 2030 under uncertain gas prices, carbon pricing
policy and electricity demand was used as a case study.
Modelling results indicate that the annual average spot
price generally reduces as the amount of renewable generation
increases due to the low operating costs of wind and PV
generation. Although there were fewer periods of demand and
supply imbalance as the renewable penetration increases, the
magnitude of the imbalance and hence average price spikes
were greater. Generally, the reduction in the average spot price
results in reduced revenues and profitability of generators and
potentially leads to insufficient revenue to meet costs,
particularly for large scale wind and PV generators.
The revenue impacts on PV and wind generation are very
severe at the high renewable penetrations considered.
Therefore, changes in market mechanisms such as increasing
market price cap may be required to ensure revenue
sufficiency and long-term resource adequacy in an energy-
only market with high renewables. Further work is warranted
to explore these issues.
There are some limitations in this study. The findings are
highly dependent on modelling and input assumptions. For the
renewable penetration greater than 60%, the proportion of PV
and wind generation chosen in the modelling may not be the
most economically optimal, resulting in higher industry costs
and almost negligible operating profits for PV. Furthermore,
there may be mechanisms other than imposing a minimum
synchronous generation constraint, which is a costly option, to
provide system inertia and frequency response at times of high
non-synchronous renewable penetrations. These limitations
represent areas for future work.
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... Despite the analysis made to the impacts on the aggregated demand profiles, no evaluation was made at nationwide scale. Other works, such as [3], analyzed the impacts of PV integration on the spot prices, but they did not consider the joint integration of PV and storage. ...
... Preliminary work by Reisz and MacGill assessed whether changes to the market price cap would enable revenue recovery in an electricity market with 100% renewables penetration (Riesz and MacGill, 2013), although this work used approximations of expected prices, and did not involve simulation of market dispatch of a high renewables generation mix. Work has been done to model the impact of increasing renewables penetrations on spot prices and generator operating profits (Vithayasrichareon et al., 2015). However, this model did not consider the impact of annual capital cost repayments on profitability. ...
Conference Paper
Full-text available
Given the declining costs of renewable energy technologies and the need to drastically reduce greenhouse gas emissions from the electricity sector, it seems likely that electricity industries around the world will have to successfully integrate very high penetrations of renewable generation. However, there is the question of how well existing wholesale electricity market arrangements might facilitate the investment necessary to achieve high renewable penetrations. In particular, the low operating costs of variable renewables such as wind and solar PV places downwards pressure on spot market prices. The likelihood of significantly higher penetrations of renewables raises the question of whether the Australian National Electricity Market (NEM)'s present wholesale spot market is capable of providing sufficient revenue to generators to allow them to recover their long run marginal costs and hence appropriately incentivise entry of new renewable generation, as well as potentially other new generation required to ensure reliable industry operation. This paper seeks to answer this question by modelling the revenue sufficiency of generators in a scenario with a high penetration of renewable energy in the NEM. This is done using PLEXOS, a commercially available electricity market simulation software package. The model used is adapted from the Australian Energy Market Operator's PLEXOS database used in the 2014 National Transmission Development Plan. The impact of different levels of strategic bidding on prices and revenue was studied using PLEXOS's Nash-Cournot competition model. Modelling results suggest that for long-run profitability, new entrant generators may need to rely on significant levels of strategic behaviour, reductions in generator capital costs, policy intervention, or changes to the present NEM wholesale market design. 1.
Full-text available
Monte Carlo simulations of gas, coal and nuclear plant investment returns are used as inputs of a Mean-Variance Portfolio optimization to identify optimal base load generation portfolios for large electricity generators in liberalized electricity markets. We study the impact of fuel, electricity, and CO2 price risks and their degree of correlation on optimal plant portfolios. High degrees of correlation between gas and electricity prices - as observed in most European markets - reduce gas plant risks and make portfolios dominated by gas plant more attractive. Long-term power purchase contracts and/or a lower cost of capital can rebalance optimal portfolios towards more diversified portfolios with larger shares of nuclear and coal plants.
Renewable technologies are often characterized as being somewhat different to ‘conventional’ generating technologies in three ways, each with different implications for electricity markets. Firstly, some have highly variable and somewhat uncertain availability, meaning that electricity markets must be designed to elicit adequate flexibility. Secondly, many have very low short-run marginal costs (operating costs), meaning that the mechanisms for managing resource adequacy must be carefully considered. Thirdly, some are nonsynchronous, meaning that grid codes and regulatory requirements must be appropriately designed. Access to flexibility can be enhanced by a range of market design choices, such as short dispatch intervals, short delays from gate closure to dispatch, large balancing areas, high demand side participation, and exposing renewable technologies to market price signals commensurate with other technologies. The design of markets for frequency control ancillary services (FCAS) also provides opportunities to increase access to flexibility, by creating active real-time markets for a wide range of FCAS, allowing renewable technologies to provide FCAS, and determining FCAS reserve requirements dynamically in real time. Mechanisms for managing resource adequacy are a source of ongoing debate, with many of the key issues having been exacerbated by the entry of renewables. Rapid market change makes investment decisions difficult, regardless of the market model applied. Ultimately, given the existence of arguably successful examples of both energy-only and capacity market designs, the choice of market model may be less important than the quality of governance with which it is implemented and maintained.For further resources related to this article, please visit the WIREs website.Conflict of interest: The authors have declared no conflicts of interest for this article.
Least cost options are presented for supplying the Australian National Electricity Market (NEM) with 100% renewable electricity using wind, photovoltaics, concentrating solar thermal (CST) with storage, hydroelectricity and biofuelled gas turbines. We use a genetic algorithm and an existing simulation tool to identify the lowest cost (investment and operating) scenarios of renewable technologies and locations for NEM regional hourly demand and observed weather in 2010 using projected technology costs for 2030. These scenarios maintain the NEM reliability standard, limit hydroelectricity generation to available rainfall, and limit bioenergy consumption. The lowest cost scenarios are dominated by wind power, with smaller contributions from photovoltaics and dispatchable generation: CST, hydro and gas turbines. The annual cost of a simplified transmission network to balance supply and demand across NEM regions is a small proportion of the annual cost of the generating system. Annual costs are compared with a scenario where fossil fuelled power stations in the NEM today are replaced with modern fossil substitutes at projected 2030 costs, and a carbon price is paid on all emissions. At moderate carbon prices, which appear required to address climate change, 100% renewable electricity would be cheaper on an annual basis than the replacement scenario.
We formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices. Our model is expressed as a two-stage stochastic mixed-integer program, which we use to compute solutions independently minimizing the expected cost and the Conditional Value-at-Risk; i.e., the risk of significantly larger-than-expected operational costs. We introduce stochastic process models to capture demand and fuel price uncertainty, which are in turn used to generate trees that accurately represent the uncertainty space. Using a realistic problem instance based on the Midwest US, we explore two fundamental, unexplored issues that arise when solving any stochastic generation expansion model. First, we introduce and discuss the use of an algorithm for computing confidence intervals on obtained solution costs, to account for the fact that a finite sample of scenarios was used to obtain a particular solution. Second, we analyze the nature of solutions obtained under different parameterizations of this method, to assess whether the recommended solutions themselves are invariant to changes in costs. The issues are critical for decision makers who seek truly robust recommendations for generation expansion planning. KeywordsGeneration expansion planning–Stochastic programming–Scenario generation–Multiple replication procedure–Solution stability
100% Renewables in Australia: Will a Capacity Market be Required?
  • J Riesz
  • I Macgill
J. Riesz and I. MacGill, "100% Renewables in Australia: Will a Capacity Market be Required?," in 3rd Solar Integration Workshop London, UK, 2013.
Strong growth, low pollution: Modelling a carbon price
  • Australian Treasury
Australian Treasury, "Strong growth, low pollution: Modelling a carbon price," Australian Government, Canberra, 2011.
Australian Energy Technology Assessment
BREE, "Australian Energy Technology Assessment 2012," Bureau of Resources and Energy Economics, Australian Government, 2012.