IEEE Power & Energy Magazine - September/October 2021 - 63

To meet reliability standards, any future with high wind
and solar generation will require dispatchable resources to
firm the renewable energy production. Energy storage located
within a REZ can smooth the profile of electricity exported,
which allows for optimal sizing of the network. When allocating
generation, extra consideration is given to where the
large-scale storage (both pumped hydro and battery energy
storage) is placed, so that it can reduce or avoid network
investment in a REZ as well as firm the VRE. Now that the
major investment decisions have been made, the next stage of
the modeling aims to optimize the dispatch outcomes for this
given set of generation, storage, and network investments.
The Time-Sequential Model
The generation and transmission decisions made by the
capacity outlook model are used as the basis for the timesequential
model. In essence, this model simulates dispatch
outcomes for the given system and mimics the dispatch process
used in real-time operations.
As the investment decisions are essentially locked in (for
our modeling), the time-sequential model allows us to go into
much greater detail in terms of portraying the physical complexities
of the power system. The model introduces a detailed
network topology and representation of the power system limit
and a Monte Carlo simulation of generation outages as well as
the supply bidding models and generation unit commitment.
The dispatch outcomes and resulting network-flow information
from the time-sequential model allow us to explore a
range of important considerations. As outlined on AEMO's
website, these considerations include
✔ possible breaches of the reliability standard
✔ feasibility of the generation and transmission outlook
when operating conditions and network limitations
are modeled
✔ number of synchronous generators online
✔ generation mix and fuel offtake
✔ utilization of the network upgrades, including interconnection
and REZ expansion
✔ impact of interregional demand diversity
✔ diversity between intermittent supply and demand
✔ the impact of unplanned generation outages.
AEMO uses these insights to modify inputs to the capacity
outlook model, such as candidate transmission options, and
validate its outcomes. With the outcomes from this capacity
expansion model, we can investigate the impact of electricity
sector investment decisions on the gas infrastructure.
The Gas Supply Model
The gas supply model is used primarily to assess gas
reserves, production, and transmission capacity adequacy
by complementing the capacity outlook models. This model
forms part of the co-optimization of both electricity and gas
sectors under the respective scenarios. The model performs
gas network production and pipeline optimization at daily
time intervals. With this capability, a range of technology
september/october 2021
solutions can be identified to reduce total system costs for
both electricity and gas infrastructures.
With the dispatch outcomes from the time-sequential
model and a capacity outlook validated in both electricity
and gas systems, we can now conduct a more detailed engineering
design and power system simulation in the engineering
assessment stage. We do this by linking the dispatch
outcomes for each interval in the time-sequential model to a
load-flow model.
The Engineering Assessment
AEMO conducts an engineering assessment to investigate
possible technical and operational challenges that may occur
given the power system and dispatch outcomes from the
capacity outlook and time-sequential models. These investigations
include network capacity, system strength, and
power system inertia. Initially, we perform network capacity
studies to ensure that the design is robust to thermal, voltage,
transient, and oscillatory stability limits across the network.
We then explore system strength, particularly within REZs,
which determines how well the power system maintains its
voltage waveform. System strength is usually weaker where
there are high penetrations of inverter-based resources that
are electrically distant from synchronous machines. Finally,
we investigate power system inertia requirements, which
require an appropriate level of synchronous inertia or its
equivalent in fast frequency response. Inertia is crucial for
ensuring frequency stability and is particularly relevant as
synchronous machines are replaced with VRE sources.
We develop solutions to these challenges to ensure a
credible and robust power system and, where required,
refine the inputs to previous models. The range of solutions
investigated includes conventional network augmentations,
the placement of synchronous machines (e.g., pumped
hydro generators) and synchronous condensers, and modern
inverter-based technologies.
In practice,
the engineering
assessments are primarily performed using dynamic and
steady-state studies.
The ultimate result of AEMO's four stages of modeling
yields a comprehensive projection of future gas and electricity
systems for a given scenario. This projection includes the technology,
amount, and locations of new generation, generally in
REZs, and the additional transmission infrastructure required
to facilitate this new generation to supply the electricity load
across the grid. It also includes the capability to investigate
specific power system operation trends on a half-hourly basis.
Delivering Economic
Efficiency with REZs
As outlined in AEMO's 2020 ISP, this modeling forecasts
that more than 26 GW of new grid-scale VRE, supported
by storage, gas-powered generation, and demand-side participation
and transmission investments, will be required to
replace power station retirements expected in the late 2020s
and mid-2030s. To enable the expected rise in renewable
ieee power & energy magazine
63
IEEE Power & Energy Magazine - September/October 2021

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - September/October 2021
