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Energy Policy 177 (2023) 113551
0301-4215/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Contents lists available at ScienceDirect
Energy Policy
journal homepage: www.elsevier.com/locate/enpol
Quantifying reserve capabilities for designing flexible electricity markets: An
Australian case study with increasing penetrations of renewables
Abhijith Prakash a,c,∗, Rohan Ashby b,c, Anna Bruce b,c, Iain MacGill a,c
aSchool of Electrical Engineering and Telecommunications, UNSW Sydney, 330 Anzac Pde., Kensington, Sydney, 2052, NSW, Australia
bSchool of Photovoltaic and Renewable Energy, UNSW Sydney, Tyree Energy Technologies Building, 229 Anzac Pde., Kensington, Sydney, 2052, NSW, Australia
cCollaboration on Energy and Environmental Markets, UNSW Sydney, Tyree Energy Technologies Building, 229 Anzac Pde.,
Kensington, Sydney, 2052, NSW, Australia
ARTICLE INFO
Keywords:
Electricity market design
Operating reserves
Balancing services
Power system flexibility
Power system reliability
National Electricity Market
ABSTRACT
Across several power systems with market frameworks, policy-makers are proposing that balancing flexibility
requirements emerging during energy transition be addressed through new reserve product markets. However,
these may introduce additional costs, constraints and complexity, and even encroach upon the functions of
existing operational practices. Thus, policy-makers need to assess and compare flexibility design options,
and quantifying system flexibility capabilities based on current and expected resource mixes can assist in
achieving this. In this article, we offer a practical method to quantify the time-varying spectrum of upwards
and downwards flexibility capabilities in systems, and subsequently apply it to historical and projected resource
mixes in two regions of the Australian National Electricity Market. Our results suggest that with higher
penetrations of renewable energy: (1) downwards flexibility margins can be exhausted around noon if wind
and solar are unable or unwilling to provide it, (2) upwards flexibility becomes more scarce during morning
and evening peak demand events and (3) a greater portion of upwards flexibility is provided by energy-limited
resources. Given these trends, we recommend that policy-makers examine how existing operational practices
can be augmented to elicit upwards flexibility provision, and that duration specifications and sustained
footroom procurement be considered for reserve products.
1. Introduction
The reliable and secure operation of power systems is contingent
upon locational and temporal balancing of active power supply and
demand. As jurisdictions progressively decarbonise electricity supply
through considerable capacity additions of variable renewable energy
(VRE) and the retirement of carbon-intensive conventional generation,
the nature of short-term risks to system balancing (i.e. those of concern
over the range of seconds to days) is changing. The most notable of
these short-term risks are (Ela et al.,2011):
•Power system variability, which includes expected changes in
the supply–demand balance. Traditionally, variability has been
associated with system load movements and fluctuations around
pre-determined generator schedules. As energy transition pro-
ceeds, system operators (SOs) are becoming increasingly focused
on managing variability that arises due to the presence of VRE.
This includes the correlated ramping of neighbouring solar PV
generation during sunrise and sunset, and that of wind generation
∗Corresponding author at: School of Electrical Engineering and Telecommunications, UNSW Sydney, 330 Anzac Pde., Kensington, Sydney, 2052, NSW, Australia.
E-mail address: abi.prakash@unsw.edu.au (A. Prakash).
following the arrival of a cold front (Lew et al.,2013;Australian
Energy Market Operator,2020g).
•Power system uncertainty, which encompasses unexpected
changes in the supply–demand balance. Beyond demand and VRE
generation forecast errors, uncertainty also includes singular or
widespread outage events that could be the result of a sudden
loss of primary energy availability, equipment malfunctions, or
common mode failures either triggered by insecure system op-
eration (e.g. significant frequency and/or voltage deviations) or
exogenous events (e.g. extreme weather events) (Redefining
Resource Adequacy Task Force,2021;Matevosyan et al.,2021;
Electricity Sector Climate Information Project,2021).
Provided that it is sufficient, leveraging the active power balancing
flexibility of a power system (defined by Heggarty et al.,2020 as
a system’s ‘‘ability to cope with variability and uncertainty’’) should
enable these short-term risks to be managed. At a particular point in
time, the total balancing flexibility capability of a power system is the
https://doi.org/10.1016/j.enpol.2023.113551
Received 6 December 2022; Received in revised form 26 February 2023; Accepted 17 March 2023
Energy Policy 177 (2023) 113551
2
A. Prakash et al.
List of Abbreviations
AEMO Australian Energy Market Operator
BESS Battery energy storage system
CCGT Combined-cycle gas turbine
DR Demand response
FCAS Frequency control ancillary services
Gas-Steam Gas-powered steam turbine
ISP Integrated System Plan
LOR Lack of reserves
MSL Minimum stable level
OCGT Open-cycle gas turbine
NEM National Electricity Market
NSW New South Wales
PASA Projected Assessment of System Adequacy
PV Photovoltaic
RERT Reliability and Emergency Reserve Trader
SA South Australia
SO System operator
SDP Synthetic daily profile
UC-ED Unit commitment and economic dispatch
VPP Virtual power plant
VRE Variable renewable energy
sum of potential flexibility contributions from resources such as gener-
ators, flexible demand and energy storage. However, the flexibility that
can actually be deployed at any given time and location is potentially
limited by:
1. Physical, economic, social and environmental constraints on the
operation of resources (Denholm et al.,2018;Gonzalez-Salazar
et al.,2018);
2. Network topology, particularly if deploying a flexibility solution
results in the violation of network constraints (Lannoye et al.,
2015;Liu et al.,2021); and
3. Operational practices. These include protocols and tools used by
the SO (which is ultimately responsible for maintaining supply–
demand balance) and electricity market design in power systems
with a market overlay (Ela et al.,2016).
Though it is well established that operational practices are crucial
to ‘‘enabling’’ balancing flexibility provision (Hirth and Ziegenhagen,
2015;Hsieh and Anderson,2017;Papaefthymiou et al.,2018), limited
attention has been given to assessing the trade-offs between practice
changes (Mays,2021). A typical design choice in power systems with
electricity markets is determining whether a balancing function should
be performed by the SO, or partially delegated to market participants
via market-based mechanisms. Proponents of market-based mecha-
nisms argue that if they are well-designed, their benefit is twofold:
appropriate incentives can unlock the efficient utilisation of latent
flexibility from existing resources whilst encouraging investment in
additional flexibility as a market-signalled need emerges. However, to
some extent, desires to maximise market benefits and minimise market
distortions need to be weighed against providing the SO with sufficient
lead-time and levers to maintain system balance during both normal
and extraordinary circumstances (Roques,2008;Prakash et al.,2022).
Establishing markets for balancing reserves offers a compromise
between SO control and market efficiency (Ryan et al.,2014;Kristov
et al.,2016). These enable the SO to set a requirement for, compet-
itively procure and then schedule system headroom (spare generation
capacity and potential load curtailment) or system footroom (potential
generation curtailment and load increase) with particular power, en-
ergy, ramping and quality-of-response (e.g. response time) capabilities
(Ulbig and Andersson,2015;Degefa et al.,2021). Whilst tailored
reserve services can be procured through tendering processes, zonal
or system-wide markets for reserve products have become increasingly
commonplace given that temporal balancing is of greater concern
in meshed networks. Additionally, ‘‘commodification’’ of capabilities
through products reduces complexity and enables the implementation
of auctions, which can improve transparency and competition and be
co-optimised with energy or other reserve product markets (Mancarella
and Billimoria,2021;Lal et al.,2021).
The changing nature of short-term risks to system balancing and
the accompanying need for greater system flexibility is leading policy-
makers to reassess the suitability of the reserve products available to
their SOs (EU-SysFlex,2019;Energy Security Board,2021;Federal
Energy Regulatory Commission,2021). Reform of reserve arrangements
can simply modify procurement practices or lead to a more signifi-
cant restructuring of available products, which includes introducing
new markets (Ryan et al.,2014). Particularly in their initial stages,
reform processes tend to justify changes on the basis of how they
might address potential threats to system balancing. This approach
is appropriate and sufficient where reserve service provision entails
specialised quality-of-response capabilities that cannot be provided
effectively or efficiently through other means (e.g. high bandwidth
control configurations required for fast frequency response provision).
However, some reserve products may ‘‘compete’’ with other design
options. For example, the purpose and timeframe of tertiary frequency
control and ramping products overlap with those of dispatch processes.
Where reserve arrangement reform encroaches on the functions of
other processes and practices, quantifying system flexibility capabilities
based on current and expected resources mixes can assist policy-makers
in assessing flexibility design options.
Reserve products also impose tangible and intangible costs. Regard-
less of cost allocation mechanisms, procuring reserves typically raises
system operation costs and thus prices paid by energy users (Hummon
et al.,2013). Furthermore, even if they offer a solution to a system
sub-problem, reserve products do not guarantee reliable operation of
the overall system and may even hinder the implementation of other
measures that can realise system flexibility (Papaefthymiou et al.,2018;
Pollitt and Anaya,2019;MacGill and Esplin,2020). For example,
valuing balancing flexibility on the scale of minutes to hours through
reserve products could mean sacrificing the benefits of better reflecting
the value of flexibility in energy prices:
1. For participants, energy market risk management is more
straightforward than managing risk in reserve product markets.
Short-term energy markets typically have greater depth and
a broader range of associated technical or financial forward
markets (Pollitt and Anaya,2019).
2. Reserve product markets often have pre-qualification criteria
and minimum offer quantities. As such, the participation of
smaller demand-side and distributed energy resources (DER)
in reserve product markets is often contingent on the involve-
ment of an intermediary aggregator, which imposes additional
transaction costs (Poplavskaya and de Vries,2019). However,
embedding the value of flexibility within the price for energy
could simplify flexibility provision through market participation
for these resources, particularly if policy-makers pursue dynamic
retail pricing or nested distribution-level markets that interface
with transmission-level markets (Kristov et al.,2016;Hogan,
2019;Mays,2021).
3. The flexibility that the SO is able to procure through reserve
products is restricted by their product specifications. Solely re-
lying on reserve products for flexibility may constrain opera-
tional outcomes. Such flexibility ‘‘discretisation’’ might also be
reflected in the resources deployed in the system should re-
serve product markets influence investment decisions (Lal et al.,
2021). Additionally, whilst reserve products can be tailored
Energy Policy 177 (2023) 113551
3
A. Prakash et al.
to a particular system’s capabilities and needs, reserve sharing
between SO jurisdictions is easier if technical specifications are
standardised (Scherer,2016).
Given these factors, quantification and comparison are therefore
needed to assess the role of reserve products, particularly where (Re-
bours et al.,2007;Ela et al.,2021):
1. Other operational practice or policy changes have the potential
to deliver greater and/or more robust flexibility benefits without
the additional costs, uncertainty and complexity of new markets;
or
2. Current market design or exogenous resource adequacy policies
(e.g. firming revenue guarantees or capacity markets) are driving
sufficient investment in flexible resources.
A plethora of metrics that quantify different aspects of system
balancing flexibility capabilities have been proposed in the literature
(Lannoye et al.,2012b;Mohandes et al.,2019;Heggarty et al.,2020).
Rather than solely quantifying flexibility capabilities, operational met-
rics typically compare short-term flexibility capabilities against a flexi-
bility requirement that is set by one of the following or a combination
thereof: rules-of-thumb, net load variability, net load forecast uncer-
tainty and/or probabilistic VRE forecasts. While a SO can use these
metrics to identify potential flexibility shortages (Zhao et al.,2016),
dimension reserve products (Dvorkin et al.,2014;Costilla-Enriquez
et al.,2023) or schedule resources (Nosair and Bouffard,2015), they
may be less useful to system designers assessing changes to practices
that leverage decentralised decision-making (e.g. energy and reserve
product markets). Broader planning-oriented flexibility capability met-
rics may be more suitable for such purposes. These include traditional
resource adequacy metrics (Stenclik et al.,2021), ‘‘inflexibility costs’’
(e.g. additional system costs due to flexibility constraints as explored
in Vithayasrichareon et al. (2017)) or ‘‘flexibility adequacy’’ metrics,
such as the insufficient ramping resource expectation proposed in
Lannoye et al. (2012a). In particular, Lannoye et al. (2012a) uses time-
sequential power system operations data to explicitly calculate the
balancing flexibility available after resources are dispatched, though
valuable chronological information is lost when the time series gen-
erated in the study are converted into probability distributions to
calculate the insufficient ramping resource expectation. By retaining
a degree of this chronological information, our methodology aims to
provide electricity industry stakeholders with a better understanding of
the time-varying ‘‘spectrum’’ of system balancing flexibility capabilities,
and thus assist them in assessing, comparing and designing potential
operational practice changes to improve flexibility in power systems
with a growing number of variable and energy-limited resources.
In this article, we offer a practical method for quantifying available
reserves and footroom (the balancing flexibility that is available after
resources are dispatched to meet system demand), and an example of
how such quantification can inform flexible electricity market design.
We provide simple extensions to the methodology developed by Lan-
noye et al. (2012a) that account for flexibility contributions from VRE
and battery energy storage systems (BESS), and market participants’
aversions to incurring cycling costs. We then use this methodology
in a case study in which we quantify time-varying available reserves
and footroom in real-world systems: two regions of the Australian
National Electricity Market (NEM). Through a 2020 baseline and two
2025 scenarios, we test four key sensitivities in these two regions:
the acceleration of large conventional generation retirement, the rate
of deployment of VRE and storage technologies, contrasting resource
mixes and operational constraints, and greater variability in operational
demand. While previous studies have tested the impact of some of
these sensitivities on the availability of total system headroom or
existing reserve products (Hummon et al.,2013;Tanoto et al.,2021;
Frew et al.,2021), our analysis offers a perspective that is focused
on quantifying a time-varying spectrum of flexibility capabilities and
thus concerned with the design of operational practices in low-carbon
power systems. Our analysis results highlight the underappreciated
need to consider mechanisms for procuring footroom, and we proceed
to discuss the implications of implementing new balancing products
on operational outcomes. Though the NEM is unique in aspects of its
operational practices and the balancing risks it faces, the methodology
and findings from this study will become increasingly relevant in other
jurisdictions given the accelerating deployment of VRE and storage and
the progressive retirement of carbon-intensive conventional generation
(International Energy Agency,2019,2021).
Section 2provides an overview of how balancing flexibility is
enabled and procured through the NEM’s operational practices and
market design. In Section 3, we describe a methodology to quantify
available reserves and footroom across deployment horizons for various
resource types. Then, in Section 4, we quantify the available reserves
and footroom in two regions of the NEM for existing resource mixes in
2020 and potential resources mixes in 2025, with two scenarios for the
latter. We then use the findings from this case study to explore the role
of reserve products in securing balancing flexibility. We conclude by
highlighting pertinent findings and recommendations to policy-makers
in Section 5.
2. Flexibility in the National Electricity Market
The Australian National Electricity Market (NEM) is a short-term
wholesale electricity market overlaid on a ∼5000 kilometre long
‘‘stringy’’ network that services the majority of eastern and southern
Australia (Australian Energy Market Commission,2022a). In 2021,
it saw a peak demand of ∼32 GW and total electricity consumption
of ∼204 TWh (Australian Energy Regulator,2022). With no explicit
capacity mechanisms or compulsory forward markets, the NEM solely
consists of a zonal real-time platform, with market regions correspond-
ing to the states of Queensland, New South Wales (NSW), Victoria,
Tasmania and South Australia (SA). Interconnection between market
regions is relatively weak and, due to the large distances involved, the
NEM is not connected to other bulk power systems (Australian Energy
Market Operator,2019c).
In the subsections that follow, we describe the operation of the
NEM with a focus on features and mechanisms that enable or explicitly
procure balancing flexibility. In particular, we discuss current reserve
arrangements in the NEM in Section 2.3 and the proposal to introduce
an operating reserve product in Section 2.3.1. The policy debate sur-
rounding the usefulness and design of this potential reserve product
provides the primary motivation for our case study in Section 4.
2.1. Market design
2.1.1. Real-time markets
The NEM is a central dispatch market that is operated by the Aus-
tralian Energy Market Operator (AEMO). On the day ahead of delivery,
market participants are required to submit non-binding offers for each
resource consisting of price-quantity pairs for energy and, optionally,
Frequency Control Ancillary Services (FCAS) (described in Section 2.3)
(Australian Energy Market Operator,2021e). Energy offers can be
priced as high as the market price cap (15,000 AUD/MW/hour during
the Australian financial year of 2020–2021) or as low as the market
floor (−1000 AUD/MW/hour). Negative pricing enables generators to
express a preference to either remain online due to significant start-
up/shut-down costs or to be dispatched as a price-taker when it is
commercially favourable to do so (e.g. to receive remuneration from
an offtake agreement). In theory, it also provides investment signals
for flexible resources alongside a relatively high market price cap (Riesz
et al.,2016;Orvis and Aggarwal,2018).
On the day of delivery, co-optimised markets for energy and FCAS
are cleared every 5 min through a security-constrained economic dis-
patch process, which produces zonal marginal prices for energy and
Energy Policy 177 (2023) 113551
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A. Prakash et al.
FCAS. There is no formal gate closure in the NEM; participants are able
to alter volumes (but not prices) in their offer up to tens of seconds
before the delivery interval (Australian Energy Market Commission,
2015;Paul McArdle,2021). In 2021, the market settlement period
was changed from 30 min (the average of prices of the preceding
six 5-minute intervals) to 5 min to better align settlement with dis-
patch and pricing (Australian Energy Market Operator,2022a). Since
resources are expected to linearly ramp between one dispatch target
and the next, the dispatch process implicitly ‘‘procures’’ some flexibility
to manage variability (Ryan et al.,2014;Australian Energy Market
Operator,2021a). As such, the NEM’s dispatch is relatively fast and
granular when compared to short-term electricity markets worldwide
(Katz et al.,2019;Silva-Rodriguez et al.,2022).
The NEM’s real-time market is also able to elicit balancing flexibility
provision from a variety of resources:
•Unlike some North American markets that permit large propor-
tions of the generation fleet to self-schedule (Ela et al.,2016;
Orvis and Aggarwal,2018), generation with a capacity above 30
MW is required to participate in the real-time market and receive
dispatch instructions (Australian Energy Market Commission,
2017). This exposes larger utility-scale resources, which make up
the bulk of the NEM’s generation capacity, to price signals that
somewhat reflect system balancing requirements.
•VRE forecasts used in dispatch can be generated by AEMO or pro-
vided by market participants; due to very late gate closure, both
are able to incorporate telemetered operational data from the
minutes preceding delivery (Australian Energy Market Operator,
2016,2018b).
•In 2021, a wholesale demand response mechanism was imple-
mented to enable larger loads (aggregated or otherwise) and
virtual power plants (VPPs) to directly participate in the energy
market1(Australian Energy Market Operator,2020i).
2.1.2. Forward markets
In the NEM, forward energy markets are voluntary and primarily
consist of the trading of electricity derivatives between market partic-
ipants. Though market participants can contract over-the-counter, the
majority of forward market activity occurs on two market exchanges
for standard products for periods up to 3 years out (ASX Energy,2021;
Australian Energy Regulator,2021). These standard products include
quarterly or annual futures, which fix a price for an agreed quantity of
energy, and caps, which are essentially call options that enable contract
purchasers (typically electricity retailers) to pay no more than the strike
price of 300 AUD/MWh for energy at the cost of a premium paid to the
seller. Contract markets in SA are considered to be relatively illiquid
compared to those in NSW, Queensland and Victoria (Australian Energy
Regulator,2022). Beyond enabling market participants to hedge real-
time market price risk, products traded on the forward markets may
‘discipline’ market participants into offering balancing flexibility to the
system. For example, a generating market participant that sells futures
and caps is likely to retain some reliable generation capacity in reserve
to avoid large payouts in the event of high real-time prices or the failure
of their other plants (Riesz et al.,2016).
2.1.3. Limitations
Despite the arguably world-leading flexible design of its real-time
markets, there are some notable limitations in the NEM and its associ-
ated forward markets:
•To date, the balancing flexibility offered by DER has primarily
been leveraged through unremunerated, last-resort curtailment of
distributed solar PV in SA by AEMO (Australian Energy Market
1Many of these resources were previously restricted to FCAS provision.
Operator,2021c) or through aggregated solar-battery VPPs. At
the end of 2021, VPPs had a registered capacity of approximately
30 MW (Kuiper,2022), a small percentage of the ∼15 GW of
distributed solar PV capacity installed in the NEM as of June 2022
(Australian PV Institute,2022).
•Aside from the procurement of footroom that is only deployed fol-
lowing frequency excursions (Section 2.3), there are currently no
mechanisms in the NEM that remunerate resources for providing
sustained downwards flexibility to the system.
•Standard derivative products have remained much the same for
decades despite changes in the NEM’s resource mix and mar-
ket dynamics. In particular, the 300 AUD/MWh strike price of
cap contracts does not necessarily reflect a resource’s operating
costs (e.g. the price of natural gas or the charging/pumping
price for BESS/pumped hydro energy storage). While a demon-
stration project trialled a market platform for derivatives de-
signed to be sold by flexible resources (e.g. a ‘‘Super Peak’’ con-
tract that enables buyers to hedge morning and evening demand
peaks), these are nascent products with small traded volumes to
date (Renewable Energy Hub,2021).
•AEMO has little visibility and no direct oversight over the vol-
untary forward markets, which are currently operated by the
financial services sector. Moreover, even if AEMO did, it would
likely be difficult for them to determine how portfolio-based
contracting might influence the operation of particular resources
(Australian Energy Market Commission,2020).
2.2. Ahead processes and operator intervention
Through several ahead processes, AEMO regularly publishes fore-
casted system and market information to assess power system reliability
and assist market participant decision-making. The processes most rel-
evant to operational decision-making include the near-term Projected
Assessment of System Adequacy (PASA) and pre-dispatch simulations:
•Using forecasts for demand and VRE, a simplified set of fore-
casted network constraints and participant-submitted resource
availabilities and energy constraints, the Pre-Dispatch PASA and
Short Term PASA (run every half-hour and hour, respectively)
both assess the maximum generation reserves available in each
region for the next 7 trading days. PASA outputs include half-
hourly available generation and system load forecasts (Australian
Energy Market Operator,2012,2020f;Australian Energy Market
Commission,2022b).
•Once day-ahead offers have been submitted by market partici-
pants, AEMO uses these offers in pre-dispatch processes alongside
forecasts for constraints, demand and VRE. Pre-dispatch simula-
tions then produce forecasts for dispatch conditions and regional
prices for energy and FCAS. These are run every half hour at
half-hourly resolution until the end of the next trading day (pre-
dispatch) and at 5 min resolution for the next hour (5 min pre-
dispatch) (Australian Energy Market Operator,2022b,2021e).
The potential impacts of demand forecast error on regional energy
prices and interconnector flows are explored through a sensitivity
analysis (Australian Energy Market Operator,2021d).
Regional balancing stress is indicated by the level of in-market
reserves, which is the total offered generation capacity in excess of
forecast regional demand.2Should the Short Term PASA or pre-dispatch
processes forecast in-market reserves below specific trigger levels,
AEMO must issue market notices that declare forecast Lack of Reserve
(LOR) conditions (Australian Energy Market Operator,2021g). Trigger
2This measure does not consider the horizon within which the capacity can
be converted to generation (i.e. the reserve horizon).
Energy Policy 177 (2023) 113551
5
A. Prakash et al.
levels are set by the maximum of either deterministic generation contin-
gencies (i.e. below N-2 for LOR1, below N-1 for LOR2 and no in-market
reserves for LOR3), or a particular confidence level of a probability
distribution of total forecasting errors generated by a Bayesian Belief
Network, which is trained on historical forecast errors and power
system conditions (Australian Energy Market Operator,2018a).
The intention of these ahead process and LOR notices is to provide
market participants with information that might elicit a response, such
as shifting planned maintenance or rescheduling flexible resources in
response to forecasted tight supply–demand balance conditions. How-
ever, if more severe LOR2 or LOR3 notices have been issued and
AEMO deems that the market response is insufficient by a certain time,
AEMO can intervene in the market by issuing directions (manual dis-
patch), activating emergency reserves procured through the Reliability
and Emergency Reserve Trader (RERT) and/or instructing transmission
network operators to shed load (Australian Energy Market Operator,
2021g,2018a).
2.3. Reserve products
Formal reserves arrangements in the NEM consist of eight FCAS
and the Reliability and Emergency Reserve Trader (RERT). In each
dispatch interval, FCAS are procured by AEMO from markets for raise
(headroom) and lower (footroom) regulation FCAS, which are used to
provide frequency control during normal operation, and three raise
and lower contingency FCAS, which deliver their full response within
6 s, 60 s or 5 min following a major imbalance event. The volumes of
FCAS procured for each dispatch interval are dynamically determined,
with regulation FCAS procurement volumes dictated by power system
time error and contingency FCAS procurement volumes typically corre-
sponding to an N-1 contingency. In the absence of regional constraints,
FCAS are procured for and from all regions of the NEM. While FCAS
provide balancing flexibility through frequency-responsive headroom
and footroom, they predominantly respond to intra-dispatch variability
and uncertainty with the expectation that deployed resources will be
relieved by 5-minute dispatch (Riesz et al.,2015;Prakash et al.,2022).
5 min contingency FCAS is an exception, given that its response may
be called upon for up to 10 min. 5 min contingency FCAS is currently
provided by a diverse range of resources (see Fig. 1).
Through the RERT, AEMO can obtain last-resort reserves given be-
tween 1 week to 1 year of notice of forecasted in-market reserves short-
falls. While procurement practices vary depending on the notice time,
RERT procurement consists of AEMO contracting with out-of-market
resources. Following forecast or actual LOR2 or LOR3 conditions and
an insufficient market response, AEMO is able to activate RERT re-
serves (Australian Energy Market Commission Reliability Panel,2020;
Australian Energy Market Operator,2021f). The RERT provides AEMO
with a last-resort mechanism to procure balancing flexibility prior to
any potential load shedding. However, resources that provide reserves
through the RERT are unable to participate in the real-time market
for the duration of their contract. After RERT reserves are activated,
market participants are remunerated based on counterfactual pricing
(i.e. dispatch without RERT), thus maintaining scarcity pricing and
potential signals for investment.
2.3.1. Operating reserves product
An inter-dispatch operating reserve product has been proposed in
the NEM. It would enable AEMO to procure headroom, which would
need to be available to the real-time market within the product horizon,
in each dispatch interval. Horizons of 5 min and 30 min were proposed
(Energy Security Board,2021;Australian Energy Market Operator,
2021h). Market bodies and participants have raised several potential
benefits of an operating reserve product:
1. It could address both inter-dispatch variability and uncertainty.
Market bodies consider that the need to address the latter may be
more material due to the growing impact of forecast uncertainty
on system balancing and the potential for high impact, low
probability power system events leading to extraordinary system
imbalances (Eggleston et al.,2021;Australian Energy Market
Commission,2021).
2. AEMO supports a 30+ min horizon, as a longer timeframe prod-
uct is likely to have a larger pool of providers and provide
participants/AEMO with more lead time prior to any poten-
tial market intervention (Australian Energy Market Operator,
2021h).
3. Through reserve constraints and potential scarcity pricing
through an operating reserve demand curve (Hogan,2013), the
product could act as an energy ‘price-adder’. This would enable
real-time market prices for energy to better reflect consumers’
preference for reliability (Cramton,2017). Although the NEM’s
market price cap is high by international standards, it is gen-
erally well below the estimated value of short-term reliability
for both residential and non-residential customers in the NEM
(Australian Energy Regulator,2019). A ‘price-adder’ could also
provide sharper investment signals for flexible resources.
The assessment of reserve capabilities to justify this new product has
been limited. AEMO has previously analysed ramping capabilities over
timeframes greater than 30 min (Australian Energy Market Operator,
2020g), the total reserve capacity available within various timeframes
across NEM regions and years (Australian Energy Market Operator,
2021h) and regularly forecasts in-market reserves (Section 2.2). How-
ever, these studies do not consider flexibility capability available after
resources are dispatched, or do not explore the time-varying spectrum
of this capability. Using the methodology outlined in Section 3, we
incorporate these elements when quantifying balancing flexibility ca-
pabilities in NSW and SA to inform an assessment of the operational
benefits of additional balancing products (Section 4).
3. Modelling available reserves and footroom
To quantify balancing flexibility capabilities, we consider headroom
and footroom that can be converted to stable active power output
within a particular time horizon. We will refer to these as available re-
serves and available footroom,3respectively. Though these metrics do not
explicitly consider whether resources are frequency-responsive, how
long a potential response can be sustained for and whether network
constraints restrain flexibility provision, calculating these quantities is
broadly useful for understanding the balancing flexibility that could be
deployed in a meshed system within operational timeframes (minutes
to hours).
3.1. Quantifying available reserves and footroom
At a given point in time and for a particular horizon, the available
reserves and footroom that a resource can offer are dependent on its
operational constraints, its synchronisation status and its active power
output. The latter two can be obtained from historical data, or as the
outputs of production-cost or market modelling.
Below, we outline a methodology for calculating system-wide avail-
able reserves and footroom (Section 3.1.5). We adapt the methodology
proposed by Lannoye et al. (2012a) to calculate available reserves and
footroom from conventional resources (coal-fired, hydro and gas-fired
3We use terminology consistent with Lannoye et al. (2015), which
quantifies available flexibility considering resource operational constraints
and realisable flexibility considering both network and resource operational
constraints. These types of flexibility exclude transient power changes from
phenomena such as inertial response.
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Fig. 1. Q4 2020 global supply curves by resource type for the raise 5 min contingency FCAS market. Each of the supply curves are truncated to the volumes of 5 min contingency
FCAS procured by AEMO across the NEM in that dispatch interval (NEM-wide mean of ∼420 MW for Q4 2020). Providers include conventional steam and hydropower generators,
an aluminium smelter, demand response (DR) aggregators, VPPs and BESS. As each supply curve is constructed from the offers of resources across the NEM (i.e. global), they do
not reflect dispatch outcomes in the presence of regional constraints. Offer and dispatch data were obtained using NEMOSIS (Gorman et al.,2018).
generation - Section 3.1.2), and propose simple extensions for calculat-
ing available reserves and footroom provided by VRE (Section 3.1.3)
and BESS (Section 3.1.4). The nomenclature used in these sections is
described in Section 3.1.1.
3.1.1. Nomenclature
3.1.1.1 Indices and sets
𝑡∈Time periods, each corresponding to the end of a 5-minute
dispatch interval in the corresponding scenario year.
ℎ∈Set of (reserve) horizons (min).
𝑟𝑐∈𝑐Set of conventional resource units.
𝑟𝑣∈𝑣Set of VRE resource units.
𝑟𝑏∈𝑏Set of BESS resource units.
3.1.1.2. Time-varying resource parameters
𝑔𝑟𝑐∕𝑟𝑣∕𝑟𝑏,𝑡 Net generation (active power output) of unit at time 𝑡(MW).
𝑔𝑓
𝑟𝑣,𝑡 Maximum generation of VRE resource unit based on primary
energy availability, i.e. 0≤𝑔𝑓
𝑟𝑣,𝑡 ≤𝑔𝑟𝑣,𝑡 (MW).
𝑔𝑟𝑐∕𝑟𝑣∕𝑟𝑏,𝑡 Maximum capacity of unit. Time-varying due to seasonal
derating and partial/full outages (MW).
3.1.1.3. Static resource parameters
MSL𝑟𝑐Minimum stable level of conventional resource unit 𝑟𝑐(MW).
Star tUp𝑟𝑐Start-up ramp up rate of conventional resource unit 𝑟𝑐. Start-
up is assumed to progress in a linear fashion (MW/min).
RampUp𝑟𝑐Upper ramp up rate of conventional resource unit 𝑟𝑐. See
Section 4.2 for an explanation of upper ramp rates
(MW/min).
RampDown𝑟𝑐Upper ramp down rate of conventional resource unit
𝑟𝑐. See Section 4.2 for an explanation of upper ramp rates
(MW/min).
3.1.1.4. Computed quantities
SUT𝑟𝑐,𝑡 Start-up time for conventional resource unit, i.e. SUT𝑟𝑐,𝑡 =
MSL𝑟𝑐−𝑔𝑟𝑐,𝑡
Star tUp𝑟𝑐
where 0≤𝑔𝑟𝑐,𝑡 <MSL𝑟𝑐(min).
𝐴𝑅𝑟𝑣,ℎ,𝑡 Available reserves from VRE resource unit 𝑟𝑣at time 𝑡for
horizon ℎ(MW).
𝐴𝑅𝑟𝑏,ℎ,𝑡 Available reserves from BESS resource unit 𝑟𝑏at time 𝑡for
horizon ℎ(MW).
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𝐴𝑅𝑂𝐹 𝐹
𝑟𝑐,ℎ,𝑡 Available reserves from offline conventional resource unit 𝑟𝑐
at time 𝑡for horizon ℎ(MW).
𝐴𝑅𝑂𝑁
𝑟𝑐,ℎ,𝑡 Available reserves from online conventional resource unit 𝑟𝑐at
time 𝑡for horizon ℎ(MW).
𝐴𝑅ℎ,𝑡 Reserves available to the system within horizon ℎat time 𝑡
(MW).
𝐴𝐹𝑟𝑣,ℎ,𝑡 Available footroom from VRE resource unit 𝑟𝑣at time 𝑡for
horizon ℎ(MW).
𝐴𝐹𝑟𝑏,ℎ,𝑡 Available footroom from BESS resource unit 𝑟𝑏at time 𝑡for
horizon ℎ(MW).
𝐴𝐹 𝑂𝑁
𝑟𝑐,ℎ,𝑡 Available footroom from online conventional resource unit 𝑟𝑐
at time 𝑡for horizon ℎ(MW).
𝐴𝐹ℎ,𝑡 Footroom available to the system within horizon ℎat time 𝑡
3.1.2. Conventional resources
The quantities of reserves and footroom that can be made available
by conventional resources are dependent on whether the resource is
online (non-zero active power output) or offline.
A conventional resource unit is considered to be online if 𝑔𝑟𝑐,𝑡 >
0. The reserves that an online conventional resource unit can make
available within the horizon ℎ(𝐴𝑅𝑂𝑁
𝑟𝑐,ℎ,𝑡) is given by:
𝐴𝑅𝑂𝑁
𝑟𝑐,ℎ,𝑡 =
⎧
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
Star tUp𝑟𝑐×ℎ0< 𝑔𝑟𝑐,𝑡 <MSL𝑟𝑐, ℎ ≤SUT𝑟𝑐,𝑡
min(
(MSL𝑟𝑐−𝑔𝑟𝑐,𝑡) + RampUp𝑟𝑐× (ℎ− SUT𝑟𝑐,𝑡 ),
𝑔𝑟𝑐,𝑡 −𝑔𝑟𝑐,𝑡
) 0 < 𝑔𝑟𝑐,𝑡 <MSL𝑟𝑐, ℎ > SUT𝑟𝑐,𝑡
min(RampUp𝑟𝑐×ℎ, 𝑔𝑟𝑐,𝑡 −𝑔𝑟𝑐,𝑡)𝑔𝑟𝑐,𝑡 ≥MSL𝑟𝑐
(1)
The three conditions in Eq. (1) reflect the following:
1. The unit is in its start-up sequence (i.e. 0< 𝑔𝑟𝑐,𝑡 <MSL𝑟𝑐) and
the reserve horizon (ℎ) is shorter than or equal to the unit’s start-
up time (SUT𝑟𝑐,𝑡). In this case, the start-up ramp rate (St art Up𝑟𝑐)
dictates the quantity of reserves that the unit can provide.
2. The unit is in its start-up sequence and the reserve horizon (ℎ)
is longer than the unit’s start-up time (SUT𝑟𝑐,𝑡). In this case, the
quantity of reserves that the unit can provide is the minimum
of the total unit ramping potential within the reserve horizon
(at rate Star tUp𝑟𝑐up to the unit’s minimum stable level, and
RampUp𝑟𝑐beyond it) and the unit’s headroom.
3. The unit is operating above its minimum stable level. The quan-
tity of reserves that the unit can provide is the minimum of the
total unit ramping potential within the reserve horizon (at rate
RampUp𝑟𝑐) and the unit’s headroom.
The reserves that an offline conventional resource unit can make
available within the horizon ℎis given by Eq. (2), which has two
conditions that resemble the first two conditions of Eq. (1):
𝐴𝑅𝑂𝐹 𝐹
𝑟𝑐,ℎ,𝑡 =⎧
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩
Star tUp𝑟𝑐×ℎ 𝑔𝑟𝑐,𝑡 = 0, ℎ ≤SUT𝑟𝑐,𝑡
min(
MSL𝑟𝑐+ RampUp𝑟𝑐× (ℎ− SUT𝑟𝑐,𝑡),
𝑔𝑟𝑐,𝑡 −𝑔𝑟𝑐,𝑡
)𝑔𝑟𝑐,𝑡 = 0, ℎ > SUT𝑟𝑐,𝑡
(2)
To ensure that flexibility quantification only considers stable
changes in active power output, footroom from conventional resource
units is defined to be the maximum downwards flexibility they can
provide without shutting down (i.e. down to their MSL). As such,
footroom can only be provided by online units operating above their
MSL (first condition in Eq. (3)):
𝐴𝐹 𝑂𝑁
𝑟𝑐,ℎ,𝑡 ={min(RampDown𝑟𝑐×ℎ, 𝑔𝑟𝑐,𝑡 − MSL𝑟𝑐)𝑔𝑟𝑐,𝑡 >MSL𝑟𝑐
0 0 < 𝑔𝑟𝑐,𝑡 ≤MSL𝑟𝑐
(3)
3.1.3. Variable renewable energy
Within the availability of their primary energy source and the
timeframes of concern in this study, VRE are considered to be highly
flexible (Nelson et al.,2018;Holttinen et al.,2021). Therefore, the
provision of available reserves (𝐴𝑅𝑟𝑣,ℎ,𝑡) and footroom (𝐴𝐹𝑟𝑣,ℎ,𝑡 ) by VRE
is not limited by ramp rates but rather by headroom and footroom:
𝐴𝑅𝑟𝑣,ℎ,𝑡 =𝑔𝑓
𝑟𝑣,𝑡 −𝑔𝑟𝑣,𝑡 (4)
𝐴𝐹𝑟𝑣,ℎ,𝑡 =𝑔𝑟𝑣,𝑡 (5)
In this study, 𝑔𝑟𝑣,𝑡 < 𝑔𝑓
𝑟𝑣,𝑡 can occur as the result of VRE curtailment
due to oversupply.
3.1.4. Battery energy storage systems
BESS are also highly flexible and, unlike other resource types, can
provide additional flexibility by switching from charging (𝑔𝑟𝑏,𝑡 <0)
to discharging (𝑔𝑟𝑏,𝑡 >0), or vice-versa. This additional flexibility can
be accounted for by including the maximum power capacity of the
BESS (𝑔𝑟𝑏,𝑡, which restricts BESS charging and discharging such that
|𝑔𝑟𝑏,𝑡|≤𝑔𝑟𝑏,𝑡 ) in the equations for available reserves (Eq. (6)) and
available footroom (Eq. (7)):
𝐴𝑅𝑟𝑏,ℎ,𝑡 =𝑔𝑟𝑏,𝑡 −𝑔𝑟𝑏,𝑡 (6)
𝐴𝐹𝑟𝑏,ℎ,𝑡 =𝑔𝑟𝑏,𝑡 +𝑔𝑟𝑏,𝑡 (7)
3.1.5. System-wide
At time 𝑡, the total reserves and footroom that can be made available
to the system within the horizon ℎare given by Eq. (8) and Eq. (9),
respectively:
𝐴𝑅ℎ,𝑡 =∑
𝑟𝑐∈𝑐
(𝐴𝑅𝑂𝐹 𝐹
𝑟𝑐,ℎ,𝑡 +𝐴𝑅𝑂𝑁
𝑟𝑐,ℎ,𝑡) + ∑
𝑟𝑣∈𝑣
𝐴𝑅𝑟𝑣,ℎ,𝑡 +∑
𝑟𝑏∈𝑏
𝐴𝑅𝑟𝑏,ℎ,𝑡 (8)
𝐴𝐹ℎ,𝑡 =∑
𝑟𝑐∈𝑐
𝐴𝐹 𝑂𝑁
𝑟𝑐,ℎ,𝑡 +∑
𝑟𝑣∈𝑣
𝐴𝐹𝑟𝑣,ℎ,𝑡 +∑
𝑟𝑏∈𝑏
𝐴𝐹𝑟𝑏,ℎ,𝑡 (9)
These equations are used to calculate system available reserves and
footroom for all reserve horizons of interest (ℎ∈) across all of the
dispatch intervals in a given scenario year (𝑡∈).
4. Case study: Two regions in the National Electricity Market
4.1. Scenarios
In this study, available reserves and footroom were quantified for
NSW and SA in calendar year 2020 and for two resource mix scenarios
in 2025 (see Table 1). The 2025 scenarios roughly correspond to the
Central and Step Change scenarios in AEMO’s 2020 Integrated System
Plan (ISP) (Australian Energy Market Operator,2020a), a least-regrets
transmission planning study that incorporates scenario-based capacity
expansion modelling (Australian Energy Market Operator,2020d).4
4The 2022 ISP was recently released (Australian Energy Market Operator,
2022a). For the planning horizon relevant to this study (i.e. to 2025), the
2022 ISP broadly reflects the outlook of its predecessor, with the exception
that it draws on extensive consultation with electricity industry stakeholders
in determining the Step Change scenario to be the most likely scenario.
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Table 1
Scenarios simulated for NSW and SA.
Scenario Description
2020 •Modelled using historical demand and existing resources
– Synchronous units (gas-fired) must run for system strength in SA
2025 Central •Based on existing policy settings at the time of 2020 ISP:
– Moderate deployment of VRE and BESS
– Distributed solar PV has moderate impact on operational demand
– Thermal unit retirements in both states
– Large hydropower capacity addition in NSW
– Fewer synchronous units must run for system strength in SA
2025 Step
Change
•More aggressive transition:
– Large deployments of VRE and BESS
– Distributed solar PV has greater impact on operational demand
– Further thermal unit retirements in NSW
– Large hydropower capacity addition in NSW
– Fewer synchronous units must run for system strength in SA
Modelling SA and NSW across these three scenarios enables four
sensitivities to be explored:
1. Conventional generation retirement. For NSW, one coal-fired
power station is retired in 2025 Central and two in 2025 Step
Change. In SA, four gas-powered steam turbine (Gas-Steam)
units and two combined-cycle gas turbine (CCGT) units are
retired between 2020 and both 2025 scenarios.
2. Increasing deployment of VRE and BESS. Additional VRE and
BESS capacity is deployed in both states between 2020 and 2025
Central in AEMO’s 2020 ISP. In the 2025 scenarios for both
states, a greater quantity of VRE (predominantly solar PV) and
BESS is installed in the Step Change scenario than in the Central
scenario. The addition of 2 GW hydro generation in NSW by
2025 reflects the expansion of the region’s largest hydro scheme
(Snowy 2.0). The capacity mix of each state in 2020 and the
changes in the mix for each 2025 scenario are shown in Fig. 2.
3. Contrast in resource mix and thus operational constraints.
In NSW in 2020, coal-fired generation is a large proportion of the
generation fleet and is complemented by hydro generation, gas-
fired generation (CCGTs and OCGTs) and VRE. In SA in 2020,
VRE (especially wind) is a significant portion of the region’s
generation fleet. SA’s synchronous generation consists of gas-
fired generation across the flexibility spectrum, some of which
must remain online to ensure there is sufficient system strength
in SA for secure operation.
4. Greater variability in operational demand due to more dis-
tributed solar PV. Operational demand is defined as the system
demand that AEMO dispatches resources to meet (i.e. exclud-
ing demand met by DER). As the capacity of distributed solar
PV in each region increases (i.e. from 2020 to 2025 Central
to 2025 Step Change), operational demand in the middle of
the day is eroded whilst ramping requirements in the morning
(downwards) but especially the evening (upwards) increase. In
other words, higher penetrations of distributed solar PV leads
to a ‘‘deeper’’ duck curve (Australian Energy Market Operator,
2020h).
4.2. Methodology
For each region and scenario, the available reserves and footroom
in the system were calculated from the results of a year-long time-
sequential market simulation implemented in the commercial elec-
tricity market modelling tool PLEXOS (Energy Exemplar,2021). The
PLEXOS market simulation consisted of a PASA phase to model main-
tenance and forced outages for conventional generation across the year,
a Medium Term Schedule phase in NSW to schedule hydro generation
according to monthly energy constraints, and a Short Term Schedule
phase that carries out unit commitment and economic dispatch (UC-ED)
at 5-minute resolution in daily steps.5
Each existing coal-fired (NSW) and Gas-Steam (SA) unit was ex-
plicitly modelled to accurately capture the consequences of partial
and full outages of large capacity units. For other resource types, the
operational constraints and attributes of individual units were averaged
and applied across all units of a resource type. This enabled clustered
UC-ED and thus reduced the computational burden of the Short Term
Schedule phase (Palmintier and Webster,2014). For baseload conven-
tional generation and gas turbines, ramp rates in each direction were
separated into a market ramp rate, which was used in the PLEXOS
market simulation, and an upper ramp rate, which was used to calculate
available reserves/footroom (Section 3.1.2). A lower magnitude ramp
rate in the market simulation (market) reflects participants’ preferences
to reduce cycling wear-and-tear due to demanding ramping during
typical operation (especially for ageing assets) (Kumar et al.,2012),
whilst using a higher magnitude ramp rate to calculate a resource’s
available reserves and footroom (upper) ensures that the total available
flexibility of a resource can be utilised if needed in a system emergency.
Both NSW and SA were modelled assuming a copper-plate net-
work with no interconnection to other regions (i.e. single bus with no
network constraints). The Short Term Schedule mixed-integer linear
program was solved using the CPLEX Optimizer (IBM,2021) with a
relative mixed-integer program gap tolerance of 0.07%. The generation
and synchronisation status of each resource was obtained from the
solution and used to calculate the available reserves and footroom for
each 5-minute interval using the equations outlined in Section 3. A
process flow diagram of the study methodology is shown in Fig. 3.
In Appendix, we outline our sources for key input data and assump-
tions (top row of Fig. 3) and provide further details regarding how
these data were used in the market simulation and/or the calculation
of available reserves and footroom.
4.3. Limitations
There are two important caveats to this study. The first is that this
study models each region in isolation — that is, resources in other NEM
regions can neither assist in meeting demand nor provide available re-
serves or footroom through cross-regional interconnectors. During typi-
cal operating conditions, it is likely that any headroom/footroom on in-
terconnectors would mean that a greater quantity of reserves/footroom
are available to a region, albeit at different horizons due to modified
dispatch patterns. For example, the inclusion of interconnectors in the
5A 12 h look-ahead was used in the SA model to avoid ‘‘end-of-horizon
effects’’ (Barrows et al.,2020), such as end-of-day decommitment of gas-fired
generation.
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Fig. 2. Capacity mix in NSW (a) and SA (b) in 2020, and additional deployments and retirements in 2025 Central and 2025 Step Change. 2020 resource mixes were adapted
from AEMO’s 2020 Inputs and Assumptions workbook (Australian Energy Market Operator,2020c). 2025 scenario resource mixes were aligned with their namesake ISP scenarios
(Australian Energy Market Operator,2020a) and include committed generation (projects that are highly likely to proceed as they have acquired land, secured financing, set a firm
construction commencement date and either finalised contracts for components or been granted planning approval) (Australian Energy Market Operator,2022b).
SA model between SA and VIC and SA and NSW6may increase the
total available reserves/footroom in SA at the cost of a decrease in
the reserves/footroom available within shorter horizons. This could
arise from local mid-merit gas generators remaining offline in favour
of inflexible but cheaper coal-fired generation in NSW and VIC.
However, modelling available reserves and footroom for isolated
regions may provide a closer approximation to reality when balancing
flexibility is scarce in a region. Under these circumstances, it is likely
that interconnector flows will already be close to their limits. This will
reduce or altogether prevent the available reserves/footroom provision
6At the time of writing, the interconnector between SA and NSW is under
construction and due to commence operation in 2025/2026 (ElectraNet,
Transgrid,2022).
from resources in neighbouring regions. Moreover, large interconnector
flows may be prevented if there is a credible risk of regional separation
(loss of synchronism between market regions due to interconnector cir-
cuit faults — a particular risk in the NEM due to limited interconnection
between market regions); at present, AEMO co-optimises interconnec-
tor flow with regional FCAS procurement (Australian Energy Market
Operator,2010). An additional consideration is that if an operating
reserve product is implemented to improve the NEM’s resilience to
supply–demand shocks, regional procurement requirements may also
limit the available reserves/footroom that can be procured over an
interconnector. As such, the modelling of isolated regions may approx-
imate actual operation when reserves/footroom are scarce and thus
most valuable to the system.
The second caveat is that this study does not explicitly model
FCAS procurement. If headroom or footroom reserved for FCAS is
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Fig. 3. Process flow for modelling available reserves and footroom for each scenario in this case study.
unable to also provide available reserves or footroom,7then mod-
elling FCAS markets would reduce the reserves and footroom that are
available within horizons less than or equal to 5 min. However, the
actual headroom/footroom reduction would depend upon the following
factors:
•Whether regional FCAS procurement constraints bind for the
modelled region. If they do not, multi-regional or NEM-wide FCAS
requirements can be satisfied by procuring FCAS in other market
regions.
•The degree to which headroom/footroom is ‘‘re-offered’’ across
sequential FCAS markets. For example, a single resource en-
abled for 10 MW across the three raise contingency FCAS mar-
kets would withdraw less system headroom than three resources
enabled for 10 MW each for a particular FCAS market.
•Headroom that is offered into the 6 s and 60 s raise contingency
FCAS market may not reflect sustained power provision. For
example, frequency response from a steam-powered turbine may
draw on steam stored in a boiler; a sustained response would
require a longer timeframe due to slower boiler dynamics.
4.4. Results and discussion
4.4.1. Synthetic daily profiles
Synthetic daily profiles (SDPs) were developed to quantify the time-
varying spectrum of available reserves and footroom for each sce-
nario. For a given horizon, the SDP value at a particular time is an
aggregate value (mean or a specific percentile) calculated from the
reserves/footroom available within that horizon at the end of that
7Exclusive headroom procurement for an operating reserve service (i.e. in-
ability to offer the same headroom in FCAS markets) is currently being
considered (Energy Security Board,2021).
dispatch interval across all days in the simulated year. In other words,
values from across the year for a given time of day are aggregated, and
these are then ‘‘stitched’’ together to form a ‘‘synthetic day’’ curve for
a particular horizon. Two aggregate values were calculated for each
horizon curve:
1. The mean. This provides a picture of the average or ‘‘typical’’
availability of reserves and footroom at different times of the
day for a particular scenario year; and
2. The bottom 1% (i.e. 1st percentile or 1-in-100 day lowest). This
measure better reflects the availability of reserves and footroom
when they are scarce and thus when they are most needed.8
In addition to an infinite horizon (which corresponds to the maxi-
mum availability), curves were calculated for 1, 5, 15, 30 and 60 min
horizons. These horizons encompass the start-up times of hydro and
flexible gas generation, and represent the likely timeframes over which
the proposed operating reserve product will be required to respond.
4.4.2. Available reserve synthetic days
Mean and bottom 1% available reserve SDPs were generated for the
NSW scenarios and for the SA scenarios (Figs. 4,5). The mean SDPs
across scenarios suggest that, on average, NSW has more than 2 GW
and SA more than 600 MW of reserves available within 5+ min. These
levels of reserves:
8More extreme percentiles (i.e. < 1%) could better reflect the tight re-
liability standards adopted in many power systems - e.g. the NEM standard
of a maximum expected unserved energy of 0.002% of the total energy
demand of a NEM region in an Australian financial year (Australian Energy
Market Commission Reliability Panel,2022). However, the use of extreme
percentiles would be more appropriate with a greater number of modelled
days (i.e. several years).
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Fig. 4. Mean (top row) and bottom 1% (bottom row) SDPs for available reserves in NSW in 2020 (leftmost column) and the two 2025 scenarios (rightmost columns).
1. Correspond to approximately 15% and 20% of peak demand
in 2020 in NSW and SA, respectively. These 5+ min ‘‘reserve
margins’’ (i.e. 5+ min reserves as a percentage of peak demand)
are comparable to lower-end reserve margins anticipated for
the summer of 2022 in North American jurisdictions (North
American Electric Reliability Corporation,2022).
2. Exceed the highest N-1 contingency in 2020 (i.e. highest LOR2
trigger level declared in the last run of Pre-Dispatch PASA prior
to delivery — see Section 2.2) by approximately 225% in NSW
and 170% in SA (Prakash,2022).
Furthermore, with additional BESS and flexible gas resources ex-
pected to be deployed, the mean 5+ min reserve margins of both
regions are higher for most parts of the day in the 2025 Step Change
scenario. Though the market simulation relied on perfect foresight
(additional uncertainty may reduce reserve margins), these results sug-
gest that reasonable quantities of reserves are available in each region
within a 5+ min horizon.
Across scenarios, the following trends are apparent in the SDPs:
1. From 2020 to the 2025 Step Change scenario, a midday peak
in the mean available reserves SDPs becomes more pronounced.
This can be attributed to the increasing displacement of conven-
tional generation by lower-cost utility-scale solar PV in dispatch
(an outcome observed by Hummon et al. (2013) and Tanoto
et al. (2021)) and the progressive erosion of daytime operational
demand due to higher penetrations of distributed solar PV.
Particularly in SA, curtailed VRE and BESS also contribute to this
reserve ‘‘surplus’’. BESS in particular are often charging during
such periods of plentiful supply and low prices, and thus are able
to offer up to double their active power rating as reserve (i.e. by
switching from charging to discharging).
2. As is particularly clear in the bottom 1% SDPs for the 2025
scenarios, the availabilities of different reserve horizons tend to
converge during periods of lower reserves or ‘‘relative scarcity’’,
which include peak demand events in the morning and evening.
The convergence may be driven by the retirement of baseload
conventional generation and higher ramping requirements in the
2025 scenarios requiring more flexible, mid-merit resources to
be online prior to and during these periods.
From this analysis, we can also gain an insight into the supply-
side dynamics of a potential operating reserve product market. The
first trend suggests that as energy transition proceeds, a reserve surplus
during the daytime could suppress the price of an operating reserve
product (a dynamic that is further explored by Frew et al. (2021)).
Moreover, the convergence of availability across horizons during peri-
ods of ‘‘relative scarcity’’ suggests that relatively inflexible but cheaper
resources are being preferentially ramped through dispatch at these
times whilst more flexible but expensive resources are left in reserve.
Since the majority of system headroom during these periods appears
to be available within 5 to 15 min, operating reserves would likely
be procured from these more flexible resources regardless of whether
the product requires availability within 5 or 30 min. As such, concerns
regarding limited providers of a 5-minute horizon product may also
apply to a 30-minute horizon product during periods of relative scarcity
(noting that several resource types in the NEM are already providing
upwards flexibility within 5 min in the NEM, as shown in Fig. 1).
4.4.3. Available footroom synthetic days
Two types of SDPs were constructed for available footroom: one for
firm footroom and the other for total footroom. The former refers to
potential footroom provision from conventional resources and BESS,
whereas the latter also includes footroom that can be provided by
curtailing VRE. Figs. 6,7show mean and bottom 1% SDPs across NSW
scenarios for firm footroom and total footroom, respectively. From the
bottom 1% SDPs in Fig. 6, it is clear that firm system footroom can
become very low in NSW in 2025 as remaining baseload conventional
generators are driven to operate closer to their MSLs. However, such
concerns could be alleviated if VRE provide footroom (Fig. 7). A similar
result was observed for the SA region.
The available footroom in the system is likely sensitive to extent
of conventional generation retirements. Further retirements may en-
able remaining conventional resources to operate at a higher loading,
thereby increasing the available footroom in the system. Regardless,
given that each region appears to suffer a lack of firm footroom for
several hours during the day in the 2025 scenarios explored in this
case study, mechanisms for procuring sustained downwards balancing
flexibility should be considered alongside those for procuring sustained
upwards balancing flexibility. One simple option would be to imple-
ment an operating footroom product, which, if VRE are permitted to
Energy Policy 177 (2023) 113551
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Fig. 5. Mean (top row) and bottom 1% (bottom row) SDPs for available reserves in SA in 2020 (leftmost column) and the two 2025 scenarios (rightmost columns).
Fig. 6. Mean (top row) and bottom 1% (bottom row) SDPs for available firm footroom (i.e. footroom provided only by ‘‘firm’’ resources: conventional and BESS) in NSW in 2020
(leftmost column) and the two 2025 scenarios (rightmost columns).
provide this service, can enable conventional generation to operate
closer to their MSL and thus reduce system operating costs and carbon
emissions (Nelson et al.,2018).
4.4.4. Short-term energy-limited reserves
While the available reserves metric does not consider the duration
for which reserve deployment can be sustained, we can infer whether
reserves are short-term energy-limited (i.e. with a duration no more
than a few hours) based on their resource type. For this analysis, BESS
reserve power was calculated based on the BESS’s state of charge at the
end of each dispatch interval and the requirement to sustain provision
for 15 min. This duration is consistent with the BESS power and capac-
ity that is reserved in SA for the possibility of loss of interconnection
(Australian Energy Market Operator,2020e). In addition, the maximum
available price-responsive demand available in each state was added to
the available reserves in each dispatch interval (assuming an emergency
response time of 5 min) to gain a better understanding of the maximum
potential contribution of demand response. This corresponded to ∼60
MW in SA and ∼290 MW in NSW, based on AEMO analysis and
forecasts in Australian Energy Market Operator (2020c). Both BESS and
DR can be considered to be short-term energy-limited reserve providers.
Though conventional generation fuel constraints (e.g reservoir schemes
and the gas system) were not modelled in this market simulation,
the contribution of conventional resources was separated into those of
thermal and hydro to assess the importance of the energy constraints
on each resource type to available reserves in NSW.
Energy Policy 177 (2023) 113551
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Fig. 7. Mean (top row) and bottom 1% (bottom row) SDPs for available total footroom (including footroom that would be provided by curtailing VRE) in NSW in 2020 (leftmost
column) and the two 2025 scenarios (rightmost columns).
Tables 2 and 3show the median percentage across dispatch intervals
in a scenario year of available reserves provided by a resources type
for NSW and SA, respectively. Whilst hydro and thermal resources
dominate 5 min horizon reserve provision in 2020 in NSW and SA,
respectively, short-term energy limited resources provide a greater
proportion of reserves in this horizon in 2025. In particular, the median
contribution of BESS to reserves available within 5 min is 16% for
NSW and 40% for SA in the 2025 Step Change scenario. As the reserve
horizon is extended to 30 min, a greater proportion of reserves are
provided by conventional resources, which may be better positioned to
sustain a response beyond the short-term.9These results indicate that
as energy transition progresses, a trade-off between reserve deployment
speed and duration develops. This trend reaffirms the value of the
sequential and hierarchical approach to reserve product design and
deployment that has been adopted in many jurisdictions (Prakash et al.,
2022). Moreover, it should be noted that unlike other mechanisms
for procuring balancing flexibility, reserve services and products can
specify duration/energy requirements and thus ensure that flexibility
provision is sustained.
4.5. The role of balancing products
It is unclear whether introducing an operating reserve product will
deliver material operational benefits to the NEM in light of the revenue
risks, complexity, and implementation and ongoing costs associated
with a new market. Instead, existing mechanisms may be able to deliver
sufficient upwards flexibility, particularly if they can be augmented:
1. Market participants with forward market obligations are strongly
incentivised to offer balancing flexibility to the market. The
premium payment offered to the seller, along with a strong
9In reality, conventional resources are also susceptible to fuel constraints,
as highlighted by the events preceding the 2022 NEM suspension (Australian
Energy Market Operator,2022c). More sophisticated modelling of thermal
coal availability, the gas system and hydro schemes, including their operation
under different climate conditions, would be required to better understand the
potential duration of available reserve provided by conventional generation.
Table 2
Median of the percentage of each resource type’s contribution to reserves available
within 5 min and 30 min in every dispatch interval for each NSW scenario year. The
median percentages are not necessarily coincident (i.e. from the same dispatch interval)
and therefore may not sum to 100%. Furthermore, some distributions are long-tailed,
so a median does not capture occasional reserve provision by a resource type (e.g.
VRE, for which all medians are 0%).
NSW resources 2020 2025 Central 2025 Step Change
5 min 30 min 5 min 30 min 5 min 30 min
BESS (15 min) 0% 0% 2% 1% 16% 14%
DR 9% 5% 5% 4% 5% 4%
Hydro 74% 43% 81% 60% 71% 61%
Thermal 18% 52% 12% 34% 8% 19%
Table 3
Median of the percentage of each resource type’s contribution to reserves available
within 5 min and 30 min in every dispatch interval for each SA scenario year. The
median percentages are not necessarily coincident (i.e. from the same dispatch interval)
and therefore may not sum to 100%. Furthermore, some distributions are long-tailed,
so a median does not capture occasional reserve provision by a resource type (e.g.
VRE, for which all medians are 0%).
SA resources 2020 2025 Central 2025 Step Change
5 min 30 min 5 min 30 min 5 min 30 min
BESS (15 min) 14% 6% 24% 10% 40% 20%
DR 7% 3% 7% 3% 5% 3%
Thermal 71% 88% 61% 84% 45% 73%
financial incentive to perform during periods of system stress,
means that derivatives such as cap contracts somewhat resemble
pay-for-performance capacity remuneration mechanisms.10 Par-
ticipants would have further incentive if contracting were made
mandatory (Mays et al.,2022), or if they increasingly resort to
contracting to hedge pricing volatility that could occur as energy
transition progresses (de Vries and Jimenez,2022).
10 However, derivatives are financial in nature and thus need not be
‘‘backed’’ by power system resources (i.e. they are not associated with any
physical obligation).
Energy Policy 177 (2023) 113551
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A. Prakash et al.
2. Market and system information and forecasts (e.g. the NEM’s
ahead processes) may be critical to ensuring that market par-
ticipants schedule resources to provide flexibility to the system.
Future work should not only seek to improve their accuracy
and their treatment of uncertainties, but also to understand
how they shape participant decision-making and thus which
enhancements could provide the most value.
However, there remain some operational benefits of additional bal-
ancing products. Nested distribution-level markets and/or real-time
market scheduling of aggregated resources have the potential to better
enable balancing flexibility from DER. However, a key insight from
Section 4.4.4 is that consideration should be given to the duration
of this flexibility. System stress could coincide with periods in which
DER owners wish to use these resources for themselves (e.g. a heat-
wave or if they are exposed to real-time market volatility to some
extent) (Roberts et al.,2020). In contrast, reserve products that specify
response durations could provide the SO with certainty that flexibility is
only procured from resources that are available for a minimum period
of time. Any duration requirements would need to be balanced against
the quantity and diversity of flexibility providers — primarily to ensure
that product markets are competitive, but also because successive
deployment of several short-term energy limited resources may be suf-
ficient to meet system balancing needs over the course of a few hours.
Furthermore, sustained footroom products might assist SOs in manag-
ing a lack of firm footroom (Section 4.4.3). Typically, energy prices
rise when upwards flexibility is scarce, thereby compensating providers
of upward flexibility. In contrast, downwards flexibility providers are
not strictly compensated through energy pricing, as oversupply could
lead to dispatch curtailing, rather than remunerating flexible resources.
Though this might mean flexible resources avoid financial losses, it
comes at the cost of footroom available to the system. Accordingly, an
‘‘operating footroom’’ product that remunerates downwards flexibility
offers a solution to the tension between dispatch incentives and the
need for system footroom.
5. Conclusion and policy implications
State-of-the-art resource adequacy assessments are closing the gap
between traditional capacity adequacy assessments, which focus on
capacity reserve margins during peak demand events, and flexibil-
ity adequacy assessments that often model chronological operations
(Stenclik et al.,2021). Yet flexibility adequacy assessments alone do
not necessarily offer a better understanding of what type of balancing
flexibility a system has and might need, and how best to make it
available to the system. As resource mixes change dramatically during
energy transition, system designers, planners and operators should
quantify balancing flexibility capabilities to gain an appreciation of the
availability of different resource types to inform operational practice
design.
By quantifying balancing flexibility ‘‘margins’’ in two sub-systems
of the Australian National Electricity Market (Section 4), we identify
potential balancing flexibility dynamics and trends in future power
systems. Firstly, systems with high penetrations of distributed and
utility-scale solar PV will likely have reserve ‘‘surpluses’’ around the
middle of the day and periods of relative reserve scarcity during morn-
ing and evening peak demand events. In such systems, the periods when
reserves are most valuable do not necessarily correspond to the periods
during which it is most efficient to curtail renewable energy generation
(due to oversupply or to obtain reserves). As such, a key recommenda-
tion for policy-makers is to consider whether reserve product markets
are needed to elicit sufficient balancing flexibility provision during
these short periods of relative scarcity, or whether adjusting energy
market settings, forward market obligations and/or market and system
information processes can achieve this. Understanding the potential
benefits of new reserve product markets is crucial because they can in-
troduce additional costs, constraints and complexity whilst encroaching
upon the functions of other operational practices. Secondly, our study
highlights the importance of placing a greater emphasis on duration,
as resources touted as essential future balancing flexibility providers
(e.g. battery energy storage, demand response) may only be able to
sustain a response for at most a few hours. Thirdly, we highlight the
need to consider footroom and the benefits of enabling renewable
energy to provide it. Footroom procurement and response duration
specifications are underappreciated by prevailing market designs, and
may be better addressed by policy-makers either modifying existing or
creating new reserve product specifications.
CRediT authorship contribution statement
Abhijith Prakash: Conceptualization, Methodology, Software, Data
curation, Formal analysis, Visualization, Writing – original draft, Writ-
ing – review & editing. Rohan Ashby: Conceptualization, Methodol-
ogy, Software, Data curation, Formal analysis, Visualization, Writing
– review & editing. Anna Bruce: Conceptualization, Methodology,
Validation, Resources, Writing – review & editing, Supervision, Project
administration. Iain MacGill: Conceptualization, Methodology, Vali-
dation, Resources, Writing – review & editing, Supervision, Project
administration.
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Data will be made available on request
Acknowledgements
The authors would like to thank:
•The Australian Energy Market Operator, the Australian Energy
Market Commission and the Energy Security Board for their
feedback on elements of this work;
•The team at WattClarity for the opportunity to present prelimi-
nary findings; and
•Christian Christiansen and Nicholas Gorman for their comments
on the original draft and revised manuscript, respectively.
This research was supported by an Australian Government Research
Training Program Scholarship and by the UNSW Digital Grid Futures
Institute.
Appendix. Data and assumptions used in market simulation
A.1. Resource ramp rates
Separate upwards and downwards ramp rates were modelled for
most resource types. For hydro generation and reciprocating engines,
maximum upwards and downwards ramp rates were sourced from GHD
(2018). For other conventional resources (coal-fired generation, Gas-
Steam, CCGT and OCGT), ramp rates in each direction were further
separated into a market ramp rate, which was used in the PLEXOS
market simulation, and an upper ramp rate, which was used to calculate
available reserves/footroom (Section 3.1.2). For these resources, the
market ramp rate was calculated using the unit ramp rates used most
Energy Policy 177 (2023) 113551
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A. Prakash et al.
Fig. A.1. Ramp rates observed (red) and used in dispatch by AEMO (blue) for a coal-fired unit in NSW in 2020. The green line denotes the ramp rate assumed by AEMO in its
2020 Inputs and Assumptions workbook and the 2020 ISP. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this
article.)
frequently in NEM dispatch11 in 2020, and the upper ramp rate was
calculated using resources’ assumed maximum ramp rates in AEMO’s
2020 Inputs and Assumptions workbook (for an example of a com-
parison, see Fig. A.1) (Australian Energy Market Operator,2020c).
Additional resources in 2025 were assumed to have the same ramp
rate characteristics as newer existing resources of the same technology
type.
A.2. Unit commitment and cycling constraints
Many existing flexible conventional resources (OCGT, reciprocating
engines and hydro generation) submit dispatch inflexibility profiles to
AEMO that contain the resource’s time to start up and reach MSL,
the MSL itself, the time required at minimum loading and the time
taken to shut down (Australian Energy Market Operator,2021b). The
most frequently offered fast start inflexibility profile of a resource in
2020 was obtained using NEMOSIS (Gorman et al.,2018) and used to
calculate its start-up rate, minimum up-time, MSL and shutdown rate.
The minimum down-time for these resources was chosen to be equal to
the minimum up-time.
For the other conventional resources (CCGT, coal-fired generation
and Gas-Steam), minimum up-times, minimum down-times and MSLs
were obtained from AEMO’s 2020 Inputs and Assumptions workbook
(Australian Energy Market Operator,2020c) and start-up rates were
calculated based on hot or warm start times (i.e. depending on the
start state of the resource after being offline for its minimum down-
time) obtained from GHD (2018) or Aurecon Australasia (2020). The
shut-down rates for these resources were calculated based on ac-
tual shutdowns, or those of similar technology types, observed in
AEMO dispatch data that was obtained using NEMOSIS (Gorman et al.,
2018).
BESS were dispatched by PLEXOS’s arbitrage algorithm subject to
charging and discharging efficiencies and maximum and minimum
state of charge constraints that corresponded to those assumed within
AEMO’s 2020 Inputs and Assumptions workbook (Australian Energy
Market Operator,2020c). Given an assumed economic lifetime of
10 years (Australian Energy Market Operator,2020c) and 3000 cy-
cles (da Silva Lima et al.,2021) for lithium-ion BESS, a constraint of
300 cycles per year was applied to BESS in each scenario.
11 The ramp rate used in dispatch by AEMO is the lesser of a telemetered
rate or a ramp rate submitted in a resource’s offer for energy, and was obtained
using NEMOSIS (Gorman et al.,2018).
A.3. Partial and forced outages
Maintenance rates, forced outage rates (partial and full) and the
corresponding mean time taken to repair were modelled for all con-
ventional generation and were sourced from AEMO’s 2020 Inputs and
Assumptions workbook (Australian Energy Market Operator,2020c).
A.4. SA synchronous generation requirement
At present, certain combinations of synchronous generators are
required to remain online for power system security in SA. Should
ahead processes indicate that the synchronous generation expected to
be online and dispatched is inadequate to provide sufficient system
strength in SA, AEMO will intervene in the market and direct addi-
tional synchronous generation online (Gu et al.,2019). The various
sufficient combinations of synchronous generation in SA are outlined
in Australian Energy Market Operator (2022d), with a decrease in
requirements/increase in the allowable asynchronous generation level
following the installation of 4 synchronous condensers (completed in
2021). To model these requirements, a must-run condition was imposed
on 3 CCGT units and 1 Gas-Steam unit in 2020, and on 2 CCGT units
and 1 Gas-Steam unit in the 2025 scenarios. These combinations reflect
a subset of the sufficient combinations outlined in Australian Energy
Market Operator (2022d).
A.5. Hydro generation monthly energy constraints
Run-of-river hydro generation and pumped hydro storage in NSW
were aggregated and modelled as dispatchable generation with monthly
energy constraints. These monthly energy constraints correspond to the
average monthly inflows for the Snowy scheme (NSW and Australia’s
largest hydro scheme) across financial years 2011 to 2018 (obtained
from Australian Energy Market Operator (2020c)). Though this model
for hydro does not account for the additional generation that could
be extracted from pumped storage, the application of monthly energy
constraints could be interpreted as modelling one pattern of run-of-river
hydro operation and/or enforcing the same reservoir level at the start
and end of each month (and thus at the start and end of each year).
Explicitly modelling reservoir schemes, inflows for individual hydro
generators and pumping opportunities for pumped hydro storage are
likely to improve the accuracy of the methodology proposed in this
work for systems with significant shares of hydropower capacity.