IEEE Power & Energy Magazine - July/August 2015 - 64

table 1. Scaling factors per generation technology
for improved ICRP transport model.

Technology

Demand
Security
Criterion

Economic
Criterion

Peaking plant (e.g., OCGTs)

Variably scaled

0%

Wind, wave, and tidal

0%

70%

Nuclear and CCS

Variably scaled

85%

Pumped storage

Variably scaled

50%

Interconnectors

0%

100%

Other

Variably scaled

Variably scaled

For each network circuit, the power flow in the peak
and year-round scenarios is compared, and the costs of
each circuit are attributed to the scenario with the highest power flow. This ensures that the marginal costs of
each transmission circuit are reflected in only one of the
two scenarios resultant charges. In this way two tariffs
are obtained: the peak security tariff and the year-round
tariff. An intermittent plant would not be exposed to
the peak security tariff. All generators will be charged
the year-round tariff in relation to their average annual
load factors while applying a series of heuristic rules and
approximations.

Stage 1: Impact Assessment

This first stage determines the whole system evolution and its
associated cost and consumers' bill when different network
charging options are modeled. The modeling horizon for this
analysis was 2030, and the modeling exercise involved iterating among a power market model, a transmission investment model, and transmission charging model until all three
converged (see Figure 3). To model the evolution of the
wholesale power market, the AURORAxmp market model
(referred to hereafter as Aurora) was used. A separate model
to optimize investment in renewable generation capacity
was created that works in tandem with Aurora. Both of these
models use assumptions on a range of fundamental market
drivers, such as the volume and characteristics of existing
generation capacity, commodity prices, the costs of new
generation capacity, and electricity demand growth, as well
as TNUoS charges. To model optimal operation and investment in the transmission system our dynamic transmission
investment model (DTIM) was used (implemented in FICO
Xpress), which takes locational generation and demand data
as an input. Using the forecast of transmission investment
from the DTIM, TNUoS charges for the period to 2030 were
computed for proposed network charging methodologies,
which were then fed to the power market modeling.
Stage 2: Cost Reflectivity Assessment

Tariff philosophy in Great Britain establishes that locational
network charges should reflect the long-run marginal cost
The modeling framework developed for analyzing transmis- (LRMC) of the network infrastructure. Hence, DTIM was
sion charging options consists of two stages, impact assess- used to estimate the LRMC of transmission infrastructure
associated with particular types of generation at different
ment and cost reflectivity assessment.
points in the network. The
methodology of calculating the LRMCs is similar
Power Market Modeling
to the improved ICRP,
Renewables Investment
Wholesale Power Market
but instead of considering
Model
Model-"Aurora"
only peak demand and two
* Optimizes Location and Timing of
* Generation Dispatch Model
generation scenarios, all
* Optimizes the Timing of Incumbent
Renewables Investment
year-round demand and
Generators' Exit Decisions
* Selects the Most "Profitable"
generation conditions (of
* Optimizes Timing and Location of
Renewables Developments, Given
New Investment
Costs and Subsidy Revenue, Etc.
which many may present network congestion)
are analyzed and used to
Transmission Modeling
calculate annuitized transmission charges for every
Transmission Charging
Transmission Investment
plant in the system.
Model
Model-"DTIM"

Modeling Framework

* Calculates TNUoS Charges, Given
Data on Transmission System
Characteristics, Costs, and
Capacity/Demand Data

* Optimizes Transmission Reinforcement Investment, Given Data on
Generation Capacity and Demand
* Makes a Least-Cost Tradeoff
Between Constraint Costs and
Reinforcement Investment

figure 3. An overview of the impact assessment modeling framework.

64	

ieee power & energy magazine	

Modeling Results
Generation Investment
Patterns

Our analysis suggests that
although the capacity mix
is broadly similar across

july/august 2015



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